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<p><strong>About the Role:</strong><br>We are seeking a detail-oriented and proactive <strong>Master Data Management Specialist</strong> to join our Supply Chain team. This role will be responsible for managing, validating, and maintaining accurate master data within our ERP system, ensuring data integrity across the organization and supporting ERP implementation and upgrades.</p><p>&nbsp;</p><p><strong>Key Responsibilities:</strong></p><ul><li><p>Implement and maintain company-wide item coding structures in alignment with ERP and business rules.</p></li><li><p>Lead and coordinate with all departments to complete ERP master data templates (items, suppliers, customers, BOMs where applicable).</p></li><li><p>Validate all submitted templates for accuracy, completeness, and compliance with coding standards.</p></li><li><p>Perform regular data cleansing and ensure non-duplication of data using validation rules and logic.</p></li><li><p>Maintain and update ERP master data including:</p><ul><li><p>Supplier Register</p></li><li><p>Customer Master</p></li><li><p>Item Master List</p></li><li><p>Other master data domains as required</p></li></ul></li><li><p>Control master data change requests and approvals according to the authority matrix.</p></li><li><p>Support ERP implementation and upgrades from a data perspective.</p></li><li><p>Coordinate with the IT ERP team on master data structure and migration.</p></li><li><p>Train end-users on data entry guidelines and standards.</p></li><li><p>Ensure compliance with internal controls and audit requirements.</p></li></ul><p></p><p><strong>Requirements</strong></p><p><strong>Qualifications &amp; Experience:</strong></p><ul><li><p>Bachelor’s degree in industrial engineering, Supply Chain, Logistics, or related field.</p></li><li><p>3–5 years of experience in ERP master data management, preferably within supply chain environments.</p></li><li><p>Hands-on experience with Oracle ERP is preferred.</p></li><li><p>Exposure to manufacturing, logistics, industrial, or trading companies is an advantage.</p></li></ul><p><strong>Technical Skills:</strong></p><ul><li><p>Strong knowledge of ERP master data domains: Item, Supplier, and Customer Masters.</p></li><li><p>Experience with ERP master data templates and data cleansing.</p></li><li><p>Advanced Excel skills (pivot tables, VLOOKUP, duplicates detection).</p></li><li><p>Understanding of item classification logic, units of measure, product hierarchy, and item attributes (procurement, inventory, costing).</p></li></ul><p><strong>Personal Skills:</strong></p><ul><li><p>Exceptional attention to detail and accuracy.</p></li><li><p>Analytical, structured, and process-oriented thinker.</p></li><li><p>Strong documentation discipline.</p></li><li><p>Excellent communication and coordination skills.</p></li><li><p>Ability to work cross-functionally across all departments.</p></li><li><p>High integrity and respect for confidentiality.</p></li></ul><p></p>

MTS Globe Egypt<br><br>Who are we?<br><br>MTS Globe is one of Europe’s leading destination management companies, operating in 66 destinations across 23 countries and serving over 11.5 million travellers each year.<br><br>What do we do?<br><br>We connect travel brands, tour operators, and hotels, delivering high-quality destination services through local expertise, strong partnerships, and advanced technology. We provide a full range of destination services - from hotel accommodation and transfers to excursions, guided tours and cruise handling.<br><br>What do we offer?<br><br>At MTS Globe, you’ll be part of an international environment where people collaborate across cultures and borders. We offer a dynamic workplace where talent can grow, learn, and build a global career.<br><br>Why join us?<br><br>Join an international and multicultural team where you can grow your career, develop globally, and be part of a company that is constantly evolving in a dynamic industry.<br><br>JOIN US AND DISCOVER YOUR FUTURE WITH MTS GLOBE GROUP!<br><br>MTS Globe is an equal opportunities employer. <br><br>Job Description<br><br>Reporting & Data Analysis Prepare and analyze daily sales reports, monthly arrivals reports, and performance dashboards. Monitor low-performance indicators and highlight risks, trends, and opportunities. Analyze hotel performance, arrivals, market share, and last-minute sales. Support forecasting and management decision-making through accurate data modeling and visualization. System Support & Operations Handle TKO system support, inquiries, and issue resolution for all departments. Support colleagues with system-related problems and process clarifications. Follow up on provider, reservation, pricing, and data-entry issues that affect reporting accuracy. Performance Monitoring Track daily sales performance and hotel results. Identify discrepancies, incorrect data entries, or unusual trends. Escalate critical issues to the Controlling Manager or Supervisor when required. Coordination & Communication Communicate with Management, Revenue, Contracting, Commercial, Yielding, and other departments. Respond to inquiries via email, phone calls, meetings, and internal systems. Support cross-department collaboration by providing reliable data and insights.<br><br>Requirements<br><br>Education<br><br>Bachelor’s degree in Tourism & Hospitality, Business Administration, Commerce, Information Technology, Data Science or similar field. <br><br>Experience<br><br>2-3 years of experience in Tourism field is a must <br><br>Languages<br><br>Strong English communication skills (B2–C1 recommended). Basic additional languages are a plus. <br><br>Technical Skills<br><br>Proficiency in: Advanced Microsoft Excel (including formulas, dashboards, and VBA is an advantage) Power BI / BI tools (recommended). Microsoft Outlook Strong data analysis and reporting skills Communication tools (Email, Phone, Whats App) <br><br>Soft Skills<br><br>Attention to detail and accuracy Time management and ability to work under pressure Strong communication and teamwork skills Problem-solving and analytical thinking Ability to follow procedures and handle repetitive, detail-oriented tasks

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Description</span><br><span></span><p><strong><u>Job Purpose:</u></strong></p><br><p>To manage the department roadmap to bring the enterprise-wide data under data governance, create a culture of continuous improvement, increasing efficiency and productivity through coaching, engagement and development of team technical skills. Also, to plan, manage and develop complex and critical analytical data quality assessment and remediation projects for data related problems to highlight the data defects and their impact on the bank strategy, different business plans and regulatory rules.</p><br><p><strong><u>Job Description:</u></strong></p><br><p><strong><u>Roles and Responsibilities</u></strong></p><br><p>1.Promote technical excellence within the team and conduct technical awareness sessions in order to ensure adopting Data Governance best practices.</p><br><p>2.Work with Business Data Stewards in order to plan and monitor complex Data Quality Analytical Projects starting with the project scope, the detailed assessment and remediation activities.</p><br><p>3.Work with IT application architectures in order to identify the integration needs between systems to avoid redundant, outdated, and low quality data.</p><br><p>4.Guide and assist the Business with the proper implementation of the data related policies and procedures in order to ensure closing all the gaps that may lead to data quality defects and mitigate financial losses, operational risks, and regulatory penalties.</p><br><p>5.Lead issues resolution through cross-departmental technology dialogues as well as address issues related to the impact of changes on various users, standardization of data entry and development of reports and data exports in order to meet the needs of the Foundation.</p><br><p>6.Identify and suggest enhancements on the data governance methods, toolsets, and processes in order to enhance data governance maturity level and adopt best practices in the market.</p><br><p>7.Monitor and report data-related risks and violations across the organization to the concerned business stakeholders in order to ensure that data quality is achieved and mitigate regulatory risks and financial losses.</p><br><p>8.Monitor, review, and develop complex data quality analytical projects, models, reports, and Dashboards to monitor and track data defects.</p><br><p>9.Develop complex data quality use cases to descript business problems, data defects trends and patterns using advanced machine learning, data mining, and data quality techniques.</p><br><p>10.Work with business and Technical leads, and data management officials in order to set the base for master data management in order to ensure the newly created data elements and systems follow the data management principles and guidelines.</p><br><p>11.Work with the technical leaders in order to identify the gaps in the data integration and migrations between the different systems, assess their impact on the enterprise data assets, business plans and regulatory requirements, and suggest and provide solutions to close the gaps.</p><br><p>12.Manage the implementation of the Enterprise Data Governance repository, measures, and KPIs used in order to build the Enterprise Data Quality Index and Data Quality Matrices.</p><br><p>13.Lead the development and research activities within the Enterprise Data Governance Department and plan for implementing the new ideas and complex use cases to ensure data governance practices are up-to-date and follow the new technology trends.</p><br><p>14.Plan, develop, own, and maintain the Enterprise Data Governance tools related to the operating model of Data governance and ensure they meet the data governance best practices.</p><br><p><strong>Policies, Processes and Procedures</strong></p><br><p>15.Implement approved department policies, processes, and procedures and monitors adherence so that work is carried out in a controlled manner</p><br><p><strong>Day-to-day operations</strong></p><br><p>16.Implement the day-to-day operations assigned for the department to ensure compliance with the established standards and procedures</p><br><p><strong>Supervision</strong></p><br><p>17.Supervise the activities and work of subordinates to ensure that all work within a specific area is carried out in an efficient manner and in compliance with the set policies, processes and procedures</p><br><p><strong>Compliance</strong></p><br><p>18.Comply with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB’s sound legal position and mitigate any potential risks</p><br><p>19.Oversee and ensure that Data-related decisions, processes, and controls subject to Data are auditable;</p><br><p>Metadata should be timely updated to reflect the real situation</p><br><p>Data architecture should be always up-to-date and reflecting the existing facts</p><br><p>Documentation should be available to support compliance-based and operational auditing requirements</p><br><br> <br> <span>Qualifications</span><br><span></span><p><strong><u>Qualifications &amp; Experience</u></strong></p><br><p>Bachelor’s degree of Information Systems, Computer Science or its equivalent.</p><br><p>7+ years’ experience in implementing and leading data governance, data management, or data analytics initiatives.</p><br><p>Experience in implementing Data Governance programs is a must.</p><br><p>Extensive experience in analyzing and solving complex business problems.</p><br><p>Extensive experience in Data Visualization tools is a must.</p><br><p><strong><u>Skills</u></strong></p><br><p>Strong Quantitative and qualitative analysis and decision making skills</p><br><p>Ability to identify and describe the complex interplay between technologies and organizational structure.</p><br><p>Demonstrated ability to (1) effectively work in an ambiguous environment, (2) effectively work in a cross-team environment, (3) succeed in a complex and dynamic work environment, and (4) work independently, directly with the business partners.</p><br><p>Strong Analytical skills with a high degree of accuracy</p><br><p>Excellent interpersonal and communication skills; ability to develop and maintain effective working relationships with persons at all levels in the organization</p><br><p>Ability to exercise good judgment and discretion, especially with regards to sensitive or confidential personnel and organizational matters</p><br><p>High energy and high endurance; ability to respond flexibly and positively in all circumstances; calm under pressure and tight deadlines</p><br><p>Strong organizational skills; ability to work independently and to priorities a heavy workload of competing assignments</p><br><br> </div>

<p><h4>Join us</h4> <p>At Vodafone, we’re not just shaping the future of connectivity for our customers – we’re shaping the future for everyone who joins our team. When you work with us, you’re part of a global mission to connect people, solve complex challenges, and create a sustainable and more inclusive world. If you want to grow your career whilst finding the perfect balance between work and life, Vodafone offers the opportunities to help you belong and make a real impact.</p> <h4>Role purpose</h4> <p>You are building the foundation that our entire software and AI stack relies on. You won't be doing basic drag-and-drop data integration—you will write distributed backend code to process large-scale batch and real-time data streams.</p> <h4>Role responsibilities</h4> <li>Distributed big data processing: Write, optimize, and debug large-scale batch data transformation jobs using Apache Spark.</li> <li>Real-time event streaming: Implement and maintain event-driven streaming pipelines using Apache Kafka to deliver low-latency data across microservices.</li> <li>ETL/ELT orchestration: Build automated ingestion pipelines using orchestrators (example: Airflow or any tool) and data transformation layers to pull from APIs and databases.</li> <li>Lakehouse governance: Structure and manage our data lakehouse, organizing schemas, enforcing data quality rules, and optimizing query performance for downstream BI and AI teams.</li> <h4>Competencies and qualifications</h4> <li>Advanced SQL: Write complex window functions and Common Table Expressions (CTEs); analyze and optimize query execution plans for speed.</li> <li>Backend programming: Strong coding proficiency in Python (for scripting/orchestration) or Scala/Java (for high-performance Spark/Kafka engineering).</li> <li>Distributed systems (Spark/Kafka): Solid understanding of how distributed compute works—specifically Apache Spark (handling data skew, transformations vs. actions) and Apache Kafka (brokers, topics, partitioning, offsets).</li> <li>Lakehouse architectures: Foundational knowledge of open table formats like Apache Iceberg or Delta Lake, and how to model data across Bronze, Silver, and Gold storage layers.</li> <li>Infrastructure: Comfortable with basic Linux admin and bash commands, monitoring and automation for data services.</li> <h4>Not a perfect fit?</h4> <p>Worried that you don’t meet all the desired criteria exactly? At Vodafone we are passionate about empowering people and creating a workplace where everyone can thrive, whatever their personal or professional background. If you’re excited about this role but your experience doesn’t align exactly with every part of the job description, we encourage you to still apply as you may be the right candidate for this role or another opportunity.</p> <h4>Who we are</h4> <p>We are a leading international telco, serving millions of customers. At Vodafone, we believe that connectivity is a force for good. If we use it for the things that really matter, it can improve people's lives and the world around us. Through our technology we empower people, connecting everyone regardless of who they are or where they live and we protect the planet, whilst helping our customers do the same.</p> <p>Belonging at Vodafone isn't a concept; it's lived, breathed, and cultivated through everything we do. You'll be part of a global and diverse community, with many different minds, abilities, backgrounds and cultures. We're committed to increase diversity, ensure equal representation, and make Vodafone a place everyone feels safe, valued and included.</p> <p>If you require any reasonable adjustments or have an accessibility request as part of your recruitment journey, for example, extended time or breaks in between online assessments, please refer to our application adjustments guidance.</p> <p>Together we can.</p></p><p></p>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span><b>Join Us</b> <br></span><p>At Vodafone, we’re not just shaping the future of connectivity for our customers – we’re shaping the future for everyone who joins our team. When you work with us, you’re part of a global mission to connect people, solve complex challenges, and create a sustainable and more inclusive world. If you want to grow your career whilst finding the perfect balance between work and life, Vodafone offers the opportunities to help you belong and make a real impact.</p><br><br><br><b>Role Purpose</b> <br><p>You are building the foundation that our entire software and AI stack relies on. You won't be doing basic drag-and-drop data integration—you will write distributed backend code to process large-scale batch and real-time data streams</p><br><br><br><b>Role Responsibilitoes </b> <br><ul> <li><strong>Distributed Big Data Processing:</strong> Write, optimize, and debug large-scale batch data transformation jobs using <strong>Apache Spark</strong>.</li> <li><strong>Real-Time Event Streaming:</strong> Implement and maintain event-driven streaming pipelines using <strong>Apache Kafka</strong> to deliver low-latency data across microservices.</li> <li><strong>ETL/ELT Orchestration:</strong> Build automated ingestion pipelines using orchestrators (example : Airflow or any tool) and data transformation layers to pull from APIs and databases.</li> <li><strong>Lakehouse Governance:</strong> Structure and manage our <strong>Data Lakehouse</strong>, organizing schemas, enforcing data quality rules, and optimizing query performance for downstream BI and AI teams.</li> </ul><br><br><b>Competencies and Qualifications</b> <br><ul> <li><strong>Advanced SQL:</strong> Write complex window functions and Common Table Expressions (CTEs); analyze and optimize query execution plans for speed.</li> <li><strong>Backend Programming:</strong> Strong coding proficiency in <strong>Python</strong> (for scripting/orchestration) OR <strong>Scala/Java</strong> (for high-performance Spark/Kafka engineering).</li> <li><strong>Distributed Systems (Spark/Kafka):</strong> Solid understanding of how distributed compute works—specifically <strong>Apache Spark</strong> (handling data skew, transformations vs. actions) and <strong>Apache Kafka</strong> (brokers, topics, partitioning, offsets).</li> <li><strong>Lakehouse Architectures:</strong> Foundational knowledge of open table formats like <strong>Apache Iceberg</strong>, or <strong>Delta Lake</strong>, and how to model data across Bronze, Silver, and Gold storage layers.</li> </ul> <ul> <li><strong>Infrastructure:</strong> comfortable with basic Linux admin &amp; bash commands, monitoring and automation for data services.</li> </ul><br><br><b>Not a perfect fit?</b> <br><p>Worried that you don’t meet all the desired criteria exactly? At Vodafone we are passionate about empowering people and creating a workplace where everyone can thrive, whatever their personal or professional background. If you’re excited about this role but your experience doesn’t align exactly with every part of the job description, we encourage you to still apply as you may be the right candidate for this role or another opportunity.</p><br><br><br><b>Who we are</b> <br><p>We are a leading international Telco, serving millions of customers. At Vodafone, we believe that connectivity is a force for good. If we use it for the things that really matter, it can improve people's lives and the world around us. Through our technology we empower people, connecting everyone regardless of who they are or where they live and we protect the planet, whilst helping our customers do the same.</p><br> <p>Belonging at Vodafone isn't a concept; it's lived, breathed, and cultivated through everything we do. You'll be part of a global and diverse community, with many different minds, abilities, backgrounds and cultures. ;We're committed to increase diversity, ensure equal representation, and make Vodafone a place everyone feels safe, valued and included.</p><br> <p>If you require any reasonable adjustments or have an accessibility request as part of your recruitment journey, for example, extended time or breaks in between online assessments, please refer to https://careers.vodafone.com/application-adjustments/ for guidance.</p><br> <p>Together we can.</p><br><br><br><br> </div>

About us:<br>Elsewedy Electric: Powering Progress, Empowering People<br>For over 80 years, Elsewedy Electric has been a leading manufacturer of electrical products and solutions, operating in 19 countries with over 18,000 employees. We offer a diverse range of products across wire & cable, electrical equipment, engineering & construction, digital solutions, and infrastructure investments. Beyond our manufacturing prowess, we're passionate about creating a positive and empowering work environment for our team. We foster a culture of diversity, inclusion, and continuous learning, providing opportunities for professional development and career growth. Our commitment to employee well-being extends beyond compensation with initiatives promoting work-life balance and overall well-being. Join us and be part of a company that's not just powering progress, but empowering people to build a brighter future.<br>This job opportunity is offered by Elsewedy Electric Graduate Development Program The program is designed for Graduates with 0-2 years of experience. The program offers various job rotations, mentorships, coaching & trainings. 2024 & 2025 Graduates are welcomed to apply.<br>Key Responsibilities:<br>Enter and update employee data accurately across HR systems. Process employee movements, including new hires, transfers, and promotions. Coordinate job changes with Internal Mobility, Organization Design, HRBP, and IT HCM teams. Ensure HR data confidentiality and compliance with data protection and privacy policies. Maintain high data quality, consistency, security, and process efficiency. Process HR data transactions within defined SLAs and in line with HR policies and SOPs. Identify opportunities for automation and digitization of HR data processes.<br>Requirements:<br>Academic Qualifications: Bachelor’s degree in Business Administration, Information Systems, or a related field. Language Proficiency: Excellent command of English. Years of Experience: 0-2 Years of experience in the same field. Computer/Technical Skills: MS Office (Excel & Word) - Google Sheets - Oracle HCM is a plus -HRIS / HR Data Management - Data Governance & Data Privacy - HR Process Digitization & Automation.

<p><span >The Senior Data Governance Officer is responsible for developing, implementing, and enforcing organizational data governance frameworks, policies, and standards that ensure data is managed securely, ethically, and in compliance with global regulations. This role serves as the primary steward of IFRC's data governance program, translating strategic governance objectives into practical policies, processes, and procedures that enable safe, ethical data utilization across the organization and protect vulnerable populations. The Data Governance Officer will establish data governance structures, manage roles and responsibilities, consider any applicable regulatory compliance, to assist with improving data quality and security, and foster a culture of data responsibility aligned with IFRC's humanitarian principles and commitment to privacy and dignity. The incumbent will support in developing the organizational approach to the distribution of data capabilities and roles.</span></p><p><strong>Data Governance Framework &amp; Strategy</strong></p><ul><li>Design and implement comprehensive organizational data governance framework, including policies, standards and compliance requirements, aligned with organizational strategy, humanitarian principles, and relevant requirements</li><li>Develop governance roadmaps outlining phased implementation of policies, standards, and controls with clear milestones and success metrics</li><li>Establish governance scope, identifying critical data assets, stakeholder roles, and organizational priorities requiring governance attention</li><li>Create and maintain governance policies covering data classification, ownership, stewardship, quality, security, access, and lifecycle management</li><li>Develop governance principles reflecting IFRC's humanitarian values, including principles of data protection, dignity, transparency, and accountability</li></ul><p><strong>Data Classification and Sensitivity Management</strong></p><ul><li>Develop classification standards and guidance for categorizing data based on sensitivity, criticality, and relevant requirements</li><li>Design and implement sensitivity labeling strategies using Microsoft Information Protection and related tools integrated within Microsoft Fabric and Purview</li><li>Create clear labeling guidelines that are easily understood across the organization</li></ul><p><strong>Data Privacy and Compliance</strong></p><ul><li>Support the Data Protection Office with implementation of IFRC s Policy on the Protection of Personal Data</li><li>Support Data Protection Officer (DPO) with governance policy input, and compliance documentation</li><li>Support Data Protection Impact Assessment (DPIA) processes for new data initiatives, ensuring privacy by design principles</li><li>Draft incident response and data breach protocols in coordination with the DPO and CISO, ensuring timely notification and mitigation in accordance with relevant requirements</li></ul><p><strong>Roles, Responsibilities and Accountability</strong></p><ul><li>Establish, assign and document organizational data governance structure with clear role definitions (Data Owners, Data Stewards, Data Custodians, Data Users) and create related RACI matrices</li><li>Support in the development of organizational approach to distribute Data, Analytics and AI capabilities and roles, across dimensions back-end, supporting and front-end as well as centralized, decentralized and emerging roles.</li><li>Provide steward training and onboarding covering governance policies, data quality management, compliance, and communication expectations</li><li>Establish governance committees and forums (e.g., Data Governance Council, domain steward groups) to facilitate coordination and decision-making</li><li>Monitor role performance and accountability, providing coaching and support to ensure effective governance execution</li></ul><p><strong>Data Quality Standards and Monitoring</strong></p><ul><li>Define organizational data quality standards and dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness)</li><li>Establish and automate data quality metrics, rules and validation checks aligned with business use cases and decision-making requirements</li><li>Develop data quality assessment and monitoring protocols identifying critical data assets and quality requirements</li><li>Establish data quality governance processes including issue identification, escalation, resolution, and continuous improvement</li><li>Monitor data quality metrics, reporting on trends and issues to governance committees and data stewards</li></ul><p><strong>Asset Inventory and Cataloging</strong></p><ul><li>Oversee the development and maintenance of enterprise data asset inventory</li><li>Establish standards for data asset documentation including business definitions, data lineage, and usage guidance</li><li>Implement and maintain Microsoft Purview data catalog systems for centralized data asset discovery, classification, and governance</li><li>Coordinate cataloging activities across the organization, ensuring data assets are properly documented and discoverable</li><li>Monitor catalog usage and provide training to support effective data discovery and asset management</li></ul><p><strong>Access Management and Information Security</strong></p><ul><li>Develop guidelines for secure information sharing with internal teams, partners, vendors, and external stakeholders, including third-party information sharing agreements</li><li>Build and maintain tools and automation to monitor and audit access patterns, investigating and addressing potential violations of access governance policies</li><li>Support implementation of technical security and privacy controls including access controls (including row and column access controls), encryption, pseudonymization, anonymization, and masking where appropriate</li><li>Coordinate with cybersecurity teams on access controls in Microsoft Fabric and related platforms</li></ul><p><strong>Governance Policy Development and Maintenance</strong></p><ul><li>Create and implement data governance policies addressing data collection, processing, storage, access, sharing, retention, and deletion</li><li>Develop policies specific to sensitive data categories (examples: beneficiary data, health information, location data) with appropriate safeguards</li><li>Monitor policy compliance, investigating deviations and implementing corrective actions</li><li>Maintain policy library and version control, ensuring staff access current guidance</li></ul><p><strong>Desired Candidate Profile</strong></p><p>The Senior Data Governance Officer is responsible for developing, implementing, and enforcing organizational data governance frameworks, policies, and standards that ensure data is managed securely, ethically, and in compliance with global regulations. This role serves as the primary steward of IFRC's data governance program, translating strategic governance objectives into practical policies, processes, and procedures that enable safe, ethical data utilization across the organization and protect vulnerable populations. The Data Governance Officer will establish data governance structures, manage roles and responsibilities, consider any applicable regulatory compliance, to assist with improving data quality and security, and foster a culture of data responsibility aligned with IFRC's humanitarian principles and commitment to privacy and dignity. The incumbent will support in developing the organizational approach to the distribution of data capabilities and roles.</p><ul><li>Design and implement comprehensive organizational data governance framework, including policies, standards and compliance requirements, aligned with organizational strategy, humanitarian principles, and relevant requirements</li><li>Develop governance roadmaps outlining phased implementation of policies, standards, and controls with clear milestones and success metrics</li><li>Establish governance scope, identifying critical data assets, stakeholder roles, and organizational priorities requiring governance attention</li><li>Create and maintain governance policies covering data classification, ownership, stewardship, quality, security, access, and lifecycle management</li><li>Develop governance principles reflecting IFRC's humanitarian values, including principles of data protection, dignity, transparency, and accountability</li><li>Develop classification standards and guidance for categorizing data based on sensitivity, criticality, and relevant requirements</li><li>Design and implement sensitivity labeling strategies using Microsoft Information Protection and related tools integrated within Microsoft Fabric and Purview</li><li>Create clear labeling guidelines that are easily understood across the organization</li><li>Support the Data Protection Office with implementation of IFRC s Policy on the Protection of Personal Data</li><li>Support Data Protection Officer (DPO) with governance policy input, and compliance documentation</li><li>Support Data Protection Impact Assessment (DPIA) processes for new data initiatives, ensuring privacy by design principles</li><li><br></li></ul>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Build the function.<br> Be its first practitioner.<br> Zero models in production.<br> That is the honest starting line, and it is also the offer.<br> DigitalZone has the parts most companies are still fighting for: a unified warehouse, data engineering and analytics teams already running, and behavioural and transaction data from a digital goods business, a ticketing platform and a regulated trading venue.<br> What it does not have is a single model deciding anything.<br> Nothing scores fraud risk, nothing forecasts demand, nothing chooses what a customer sees next.<br> You are the first data science hire.<br> You will build the team, set how it works, and personally be the data scientist for Platform: payments, identity and marketing technology.<br> You report directly to the executive who owns Product and Data, so the distance between your finding and a decision is one conversation.<br> It also means there is nobody to hide behind when a model does not work.<br> The hard part Fraud in a cash-heavy market with agent networks and chargeback exposure does not sit still.<br> Your model will be adversarial from day one, and the threshold you set is a live tradeoff between loss prevented and customers blocked.<br> That call will be yours to make and yours to defend.<br> Identity is the other half.<br> Customer records arrive from several products and a partner login, and resolving them into one trustworthy profile is what makes personalisation, risk scoring and half the company's roadmap possible.<br> Nobody has done it yet.<br> What you'll do Build the function from one person to a team: charter, hiring, and the standard for how a model is specified, validated, shipped, monitored and retired.<br> Own fraud detection as a product: transaction and payment risk scoring, chargeback and refund abuse, account takeover, promo abuse, agent and merchant anomaly detection.<br> Set the thresholds with fraud and operations, and defend the loss-versus-friction tradeoff.<br> Own the modelling side of identity management: identity resolution and record linkage across sources, deduplication and match confidence, profile completeness scoring, and the unified customer profile that both personalisation and risk read from.<br> Drive the next-best-action and segmentation layer behind personalised customer communication.<br> Get models into production and keep them there, with the cloud team on serving and MLOps: monitoring, drift, retraining, rollback, and one measurable business outcome per model you are willing to be judged on.<br> Own the planning models: demand and stock forecasting, predictive budget, category and SKU level targets, churn and lifetime value.<br> Own experiment design across product and marketing: hypothesis, minimum detectable effect, decision rule, and honest readouts including the null ones.<br> Agree the boundary with data engineering and analytics.<br> It is not settled today.<br> Settling it is part of the job.<br> Bring stakeholders a decision, not a notebook.<br> Definitions come from the company metric contract, and an unvalidated figure does not become a fact by appearing in a deck.<br> Immediate, large-scale impact on a high-growth business Top-of-the-market compensation packages Work alongside top regional talent, with team members from Talabat, Careem, Etisalat, and more What you'll bring 7+ years in data science, 2+ leading or mentoring scientists.<br> Having been the first or second scientist somewhere counts double.<br> Models in production with a measured business outcome.<br> Hard requirement: live traffic, monitored, effect quantified.<br> A portfolio of notebooks is not this.<br> Fraud or risk modelling depth, plus at least one of: identity resolution and entity matching, forecasting, churn and lifetime value, personalisation, uplift modelling, causal inference.<br> Strong SQL against large transaction and event data in a columnar warehouse, strong Python, and code someone else can run next quarter.<br> Experimental and causal rigour, including quasi-experimental methods where randomisation is not possible.<br> Consumer fintech or payments fluency: transactions, funnels, cohorts, fraud patterns.<br> The ability to align product, engineering, marketing and commercial on one problem definition before any modelling starts.<br> AI-assisted working, with the rule that you never ship output you cannot explain.<br> Nice to have Built a data science function from zero, including the first hires and the first deployment path.<br> Fraud or payment risk in a market with cash-heavy behaviour, chargeback exposure or agent networks.<br> Identity graph, KYC or customer data platform experience.<br> MENA or emerging-market consumer data, with the data quality reality that comes with it.<br> Arabic. What we will not pretend There is no team yet, no serving infrastructure you can lean on, and no data science rubric to hide behind.<br> The first six months are you, a warehouse and a lot of stakeholder conversations.<br> What you get in return is a blank page at a company that has already decided models matter, and the rare chance to be judged on outcomes you defined yourself.<br> If you want to join a mature data science org, this is the wrong role.<br> If you want to be the reason one exists, apply.<br></span> </div>

Job Description:• Own the day-to-day administration of all production databases — installation, configuration, patching, user management, and routine maintenance.• Monitor database health continuously — performance metrics, resource utilization, replication lag, connection counts, lock contention, and storage growth — with proactive alerting before thresholds are breached.• Manage database user accounts and access controls — implement and enforce least privilege access, role separation, and periodic access reviews.<br>• Design, implement, and manage database high availability solutions — • Define and maintain the database disaster recovery strategy — RPO and RTO targets, tested failover procedures, and documented recovery runbooks.• Implement and manage automated backup solutions • Test backup and recovery procedures regularly — restore drills must be documented, scheduled, and performed, not just assumed to work.• Implement and manage database migration tooling — ensuring schema changes are version-controlled, repeatable, and automatically applied through the deployment pipeline.<br><br>Work with application development teams — review data models, advise on schema design, and ensure applications interact with the database efficiently.• Work with the data engineering team — define data pipeline requirements, ensure ETL processes do not degrade production database performance, and maintain clean data contracts.• Work with the infrastructure team — define storage requirements, I/O performance specifications, and server configuration standards for database hosts.• Work with the Dev Ops team — integrate database change management into the CI/CD pipeline, define automated testing requirements for database changes.<br>Job Responsibilities:Experience• 4 to 7 years of hands-on database administration experience — in production environments, not just development or support roles.• Demonstrable experience with Postgre SQL in production — administration, performance tuning, replication, and high availability. Postgre SQL is the primary platform.• Experience implementing and managing database HA solutions — replication, automatic failover, and connection pooling in production.• Experience in a regulated environment — financial services, healthcare, or equivalent — with understanding of audit requirements and data security controls.• Experience participating in or supporting database disaster recovery testing — not just designing the plan, but executing and validating it.<br>Technical Skills• Postgre SQL — administration, configuration, performance tuning, replication (streaming, logical), Patroni, PgBouncer, pg Back Rest.• SQL — advanced query writing, execution plan analysis, index design, and query optimisation.• Database monitoring — pg_stat views, Prometheus postgres_exporter, or equivalent monitoring stack integration.• Backup and recovery — pg Back Rest, pg_dump, WAL archiving, point-in-time recovery.• Database security — role management, row-level security, SSL/TLS configuration, audit logging.• Linux system administration — sufficient to manage database server operating systems, storage configuration, and performance tuning at the OS level.• Shell scripting — Bash or Python for automation of routine DBA tasks.• Additional database platforms are an advantage — Oracle, SQL Server, MySQL — particularly for understanding cross-platform migration and integration scenarios. Education• Bachelor's degree in Computer Science, Information Technology, or a related field. Arabic and English fluency — written and spoken

Duties & Responsibilities:Data Architecture and Strategy: 1. Develop and maintain the organization's data architecture strategy and roadmaps. 2. Define data standards, principles, and best practices. 3. Establish guidelines integrating disparate data sources and systems to ensure seamless data flow and accessibility across QNBAA. Data Pipeline Development and Modelling: 4. Lead the design and development of databases and pipelines to guide data ingestion, transformation, and loading from various sources into data repositories. 5. Define metadata principles to provide context and understanding of the data stored within systems. 6. Implement scalable and efficient ETL processes to handle large volumes of data. 7. Collaborate with data scientists, analysts, and other stakeholders to understand data requirements. 8. Creating conceptual, logical, and physical data models to represent data structures and relationships within the organization. 9. Designing big data principles for storing and analyzing structured data to support business intelligence and analytics. Data Collection and Integration: 7. Identify and inventories data sources, including databases, APIs, and external data providers. 8. Collaborate with data scientists and analysts to determine the most suitable methods for data extraction, transformation, and loading (ETL). Data Transformation: 9. Coordinate execution of data cleaning, transformation, and enrichment to ensure that the data is in a usable format for analysis. 10. Collaborate with data scientists and analysts to define data transformation rules and logic. Data Accessibility Management: 9. Oversee the design and maintenance of data structures and repositories (e.g., data warehouses, data lakes) that make data easily accessible to data scientists and analysts. 10. Ensure data is organized and stored efficiently for analytics and reporting purposes. 11. Monitor data warehouse performance and optimize data storage and retrieval to support fast querying and analysis. 12. Establish guidelines for the implementation of security measures to protect sensitive data, including encryption, access controls, and regular security audits. 13. Designing and implementing data storage, processing, and management solutions on cloud platforms while ensuring security and compliance. 14. Establish processes for the entire data lifecycle, including creation, storage, usage, archival, and deletion, ensuring compliance with legal and regulatory requirements. Performance Optimization: 12. Monitor the performance of data pipelines and data storage solutions and optimize them as needed. 13. Collaborate with data scientists and analysts to ensure that query performance meets expectations. Data Quality and Governance: 14. Establish data quality standards and data governance practices. 15. Implement data validation and cleansing processes. 16. Ensure data accuracy, consistency, and compliance with regulations. Team Leadership: 17. Recruit, mentor, and manage a team of data engineers and data integration specialists. 18. Foster a culture of collaboration, innovation, and continuous improvement. 19. Set clear goals and expectations for team members. 20. Conduct regular performance evaluations and provide feedback. Technology Evaluation and Implementation: 21. Stay informed about emerging data engineering technologies and trends. 22. Evaluate and select appropriate tools and technologies for data engineering tasks. 23. Lead the implementation of data engineering solutions. Collaboration and Communication: 24. Collaborate with cross-functional teams, including business teams, data scientists, analysts, and IT teams, to understand data requirements and ensuring IT solutions support these requirements. 25. Communicate data engineering strategies, progress, and challenges to senior management and stakeholders. 26. Collaborate with data scientists and analysts to troubleshoot data-related issues and find solutions to complex data problems, refine data workflows and optimize data processes. 27. Document and maintain data catalogs so as to ensure that all teams have a clear understanding of data structures, transformations, and pipelines. Vendor and Budget Management: 28. Manage relationships with data engineering tool vendors and service providers. 29. Develop and oversee the data engineering budget, ensuring cost-effectiveness and resource allocation. General 30. Ensure the socialization and adoption of the right data culture to uplift data maturity across different lines of businesses. Collaborate with cross functional teams to develop data literacy/skills training initiatives and create awareness about the importance of data governance and data management. 31. Ensure correct adoption of policies and procedures to guarantee that QNB ALAHLI business is conducted in compliance with Local Laws, Internal rules and regulations, as well as, International Standards. 32. Ensure the correct functioning and implementation of Permanent Supervision system, compliance, operational risk & workplace success guidelines whenever & wherever possible.

To design, build, and maintain scalable data infrastructure and pipelines that enable reliable, efficient, and secure data flow for analytics, business intelligence, and AI/ML initiatives. Reporting directly to the Data Analytics & AI Manager, the role ensures that data from ERP (Oracle), production systems, supply chain, and other sources is ingested, transformed, and made available for data-driven decision-making across FCG's frozen food manufacturing and export operations.<br>RESPONSIBILITIESDesign, develop, and maintain robust ETL/ELT pipelines to ingest data from multiple sources (Oracle ERP, production systems, WMS, IoT sensors, external APIs). Build batch and real-time data pipelines using orchestration tools (Airflow, Prefect, or similar) to support analytics and AI/ML workloads. Ensure data quality, consistency, and reliability throughout the pipeline lifecycle. Design and manage data warehouse and data lake solutions (Snowflake, Big Query, Redshift, or Azure Synapse) optimized for analytics and AI consumption. Implement dimensional modeling (Kimball/Inmon) and data vault methodologies to support BI dashboards and AI feature stores. Optimize storage, partitioning, and query performance for large-scale datasets used by data analysts and data scientists Build and maintain feature stores and training datasets for machine learning models. Collaborate with data scientists to understand data requirements for predictive modeling, forecasting, and optimization algorithms. Implement data transformation logic for model training, validation, and inference pipelines. Integrate data from internal systems (Oracle ERP, WMS, HRIS) and external sources (supplier portals, logistics APIs, market data feeds). Build and maintain API-based data extraction and ingestion services for analytics consumption. Collaborate with application teams to ensure seamless data flow between operational systems and the analytics environment. Implement data quality checks, validation rules, and monitoring within pipelines to ensure accuracy for BI and AI use cases. Ensure data lineage, cataloging, and metadata management for full traceability. Collaborate with the Data Governance team to enforce data standards and security policies. Tune SQL queries, pipeline performance, and data processing jobs to meet SLA requirements for dashboards and AI model refreshes. Optimize resource utilization and cost efficiency for cloud data platforms. Troubleshoot data pipeline failures and ensure timely resolution with minimal impact on analytics users. Work closely with Data Analysts, BI Developers, and Data Scientists to understand data requirements for dashboards, reports, and models. Provide clean, well-documented, and timely datasets for analytics and AI/ML initiatives. Mentor junior data engineers and contribute to best practices within the Data & Analytics team. Maintain technical documentation for data pipelines, schemas, data dictionaries, and feature stores. Ensure compliance with data security, privacy, and retention policies.<br><br><br>REQUIREMENTSBachelor's degree in Computer Science, Data Engineering, Information Systems, or related field. Master's degree is a plus.5–8 years of experience in data engineering, ETL/ELT development, or data warehousing. Experience in manufacturing, FMCG, or food processing industries is preferred. Proven experience supporting analytics, BI, and AI/ML initiatives is highly desirable. Experience with ERP data extraction is a strong advantage. Programming: Advanced Python (Pandas, PySpark) and SQL. ETL/Orchestration: Airflow, dbt, Prefect, or similar tools. Cloud Platforms: AWS (S3, Redshift, Glue), Azure (Data Factory, Synapse), or GCP (Big Query, Dataflow). Data Warehousing: Snowflake, Big Query, Redshift, or Azure Synapse. Big Data Technologies: Spark, Kafka (preferred). Version Control: Git (Git Hub/Git Lab). o Containerization: Docker (preferred). Feature Stores: Experience with Feast, Tecton, or similar (preferred). Strong analytical and problem-solving abilities. Excellent communication and collaboration skills, especially with analytics and data science teams. Attention to detail and commitment to data quality. Ability to work independently and handle multiple priorities.

About the Role We are seeking an experienced Temenos Data Analyst to support data analysis, validation, and reporting initiatives related to our Temenos core banking ecosystem. In this role, you will work closely with engineering, data, and business teams to analyze and validate data extracted from core banking systems, ensuring high levels of data accuracy and consistency. The ideal candidate will have strong experience working with Temenos data structures, advanced SQL skills, and the ability to translate business requirements into meaningful analytical outputs. Key Responsibilities Temenos Data Management Configure and manage Temenos Data Source (TDS) including provider setup, parameterization, and data imports/exports. Implement business rules using plugins and manage provider configurations. Ensure proper end-to-end data flow from Temenos core systems. Data Analysis & Validation Analyze, validate, and reconcile data extracted from Temenos systems. Ensure data accuracy, completeness, and consistency across analytical outputs. Investigate data discrepancies and perform root cause analysis. Data Querying & Reporting Design and execute complex data queries using Oracle Database, SQL, and PL/SQL. Support reporting and analytical requirements across business teams. Data Processing & Automation Support data preparation and transformation using Python (Pandas) where required. Automate data-related tasks using scripting tools. Collaboration Work closely with business and reporting teams to translate requirements into data mappings and analytical outputs. Participate in Agile delivery processes, including backlog refinement and testing activities. Support & Documentation Provide L2/L3 support for data-related issues within the Temenos ecosystem. Maintain documentation for data definitions, mappings, and usage guidelines. Required Qualifications Experience2–5 years of experience in data analysis or banking data environments Proven experience working with Temenos core banking data Technical Skills Temenos Data Source (TDS) Oracle Database SQLPL/SQLData validation and reconciliation Data Engineering & Analytics Python (Pandas) for data processing Data mapping and transformation Understanding of banking data flows and core banking integrations Professional Skills Strong analytical and problem-solving abilities Strong documentation and communication skills Experience working in Agile development environments Preferred Qualifications Experience working with Temenos core banking platforms Experience supporting banking reporting and analytics initiatives Familiarity with data warehousing or analytics platforms Experience working in digital banking or fintech environments

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Nawy Proptech is in search of a highly motivated and talented Data Engineer to become a valuable addition to our dynamic team.<br> As a Data Engineer at Nawy, you will play a key role in data management, ensuring data quality, integrity, and modeling.<br> We are seeking an individual with a strong technical background, hands-on experience with data processing technologies, and a passion for building robust and efficient data solutions.<br> Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and design appropriate solutions.<br> Work closely with IT and infrastructure teams to deploy and maintain data storage and processing systems, including databases, data warehouses, and cloud infrastructure.<br> Design, build, and maintain robust, scalable data pipelines to ingest, process, and transform data from various sources, including internal databases and external APIs.<br> Optimize data pipelines for performance, scalability, and reliability, ensuring timely and accurate delivery of data.<br> Implement data validation and quality checks to identify and rectify any issues in data pipelines, ensuring data integrity.<br> Explore and evaluate new technologies, tools, and frameworks to improve data engineering processes and capabilities.<br> Perform data modeling and schema design to support analytical and reporting requirements.<br> Design and build data marts and business layers to facilitate efficient data access and analysis.<br> Bachelor’s degree in Computer Science, Information Systems, or other related technical field or equivalent work.<br> Proven experience as a Data engineer or similar role, with at least 1 years of experience.<br> Advanced SQL knowledge/experience - complex queries and query optimization.<br> Proficiency in programming languages such as Python.<br> Experienced in the development of data warehouses and proficient in data modeling.<br> Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.<br> Experience in building data streams using technologies such as Kafka, Flink, or Spark Streaming is considered a plus.<br> Strong problem-solving skills and attention to detail.<br> The ability and willingness to learn new technologies on the job.<br> Self driven, highly motivated, fast learner, ambitious and creative.<br></span> </div>

Responsible for leading and managing the organization’s data engineering function, ensuring the development and delivery of scalable, reliable, and high-performing data platforms and pipelines aligned with business objectives. This role focuses on defining data engineering standards, practices, and frameworks to enable efficient data ingestion, transformation, integration, and processing across cloud and on-premises environments. The Data Engineering Manager leads and mentors data engineers, oversees engineering projects and delivery, and collaborates closely with data architecture, governance, analytics, and business teams to ensure data solutions are secure, governed, high-quality, and optimized for performance and scalability.<br>Data Architecture & Design:Develop and maintain a scalable, secure, and high-performance data architecture aligned with business needs. Ensure seamless data flow between systems, integrating structured and unstructured data sources. Define and implement best practices for data storage, processing, and security. Select and optimize data platforms, tools, and technologies for efficiency and cost-effectiveness.<br>Data Modeling:Design and implement data models (dimensional, relational, Data Vault, NoSQL) to support operational and analytical use cases. Ensure consistency, accuracy, and integrity of data models across the organization. Continuously refine models to improve performance and adaptability to evolving business requirements. Establish and enforce data modeling standards and governance.<br>Data Engineering and Pipeline Development:Build, optimize, and maintain automated ETL/ELT pipelines to process large-scale data. Troubleshoot, debug and upgrade existing ETL solutions. Enhance data infrastructure to support efficient storage, retrieval, and transformation. Implement automation to reduce manual processes and increase workflow efficiency. Ensure data pipelines are robust, scalable, and support real-time and batch processing. Identify data discrepancies and develop data metrics.<br>Innovation & Technology Adoption:Stay up to date with the latest trends in data architecture, engineering, and analytics to identify and implement innovative solutions. Research and integrate cutting-edge tools and platforms to enhance data management capabilities and streamline processes. Encourage a team culture focused on continuous improvement, supporting experimentation with new technologies and methodologies. Develop and execute a technology roadmap to ensure the team is using the best tools available to meet both current and future data needs.<br>Optimization & Performance Management:Define and enforce data governance policies, ensuring compliance with GDPR, and other regulations. Monitor and optimize database performance, query execution, and storage efficiency. Manage data quality, metadata, and lineage to ensure reliability and transparency. Implement proactive monitoring and alerting for data infrastructure health. Monitor performance and quality control plans to identify improvements.<br>Collaboration with Cross-Functional Teams:Work closely with data scientists, business analysts, and BI teams to understand data requirements and design solutions that enable advanced analytics, reporting, and decision-making. Act as a bridge between business units and IT, ensuring that data architecture and engineering solutions meet both technical and business needs. Lead efforts to optimize data workflows and processes across departments, driving efficiency and alignment in data operations.<br>Development of Employee’s Performance:Provides support for professional development of direct reports. Direct the hiring, or performance evaluation of the team and oversee their daily activities. Motivate, organize and encourage teamwork within the workforce to ensure targets are met or exceeded. Monitors Training Effectiveness Reports on Training Activities<br><br>Educational Requirements: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field. Advanced degrees provide deeper technical and strategic insights, enabling the professional to drive complex data solutions and innovations at the organizational level.<br>Special Certification or Training Required: Certified Data Management Professional (CDMP) – Preferred for expertise in data architecture and governance is preferred. Microsoft Certified: Azure Data Engineer Associate or AWS Certified Data Engineer Associate is preferred.<br>Required Industry Experience: 8+ years of experience in Data Architecture, Data Engineering, and Data Modeling with a proven track record of leading large-scale enterprise data initiatives. Expertise in designing, implementing, and optimizing complex data ecosystems in hybrid environments. Strong background in data governance, security, compliance, and risk management across enterprise systems. Hands-on experience managing data integration, migration, and transformation projects in a large-scale business setting. Technological Requirements: Deep expertise in architecting, optimizing, and managing both relational (e.g., Oracle, Postgre SQL) and NoSQL (e.g., Mongo DB, Cassandra) databases. Proven track record of designing and implementing scalable, enterprise-level Data Warehouse (DWH) architectures. Expert-level proficiency in writing and tuning complex SQL queries, stored procedures, functions, and triggers for high-performance data processing. Strong expertise in data modeling techniques (conceptual, logical, physical, dimensional, and semantic models). Deep understanding of data integration patterns, streaming data platforms, and event-driven architectures, and quality frameworks. Proficiency with cloud data platforms (AWS, Azure, GCP) and modern ecosystem tools (Databricks, Snowflake, Big Query, Synapse). Strong command of ETL/ELT frameworks, APIs, and microservices for data delivery. Proven expertise in architecting and managing data lakes and data warehouses for big data environments.<br>Language Requirements: Fluent in English.

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <p><strong>JOB DETAILS:</strong></p><br><p>Position Title: Data Engineer</p><br><p>Reports to: Data and AI Manager</p><br><p>Business Line/ Department: Data and Automation/it</p><br><p><strong>3. JOB PURPOSE: </strong></p><br><p> The Job Holder is Responsible to Builds and maintains data pipelines, ETL/ELT processes, and data integration solutions that move and transform data across the data platform to support analytics, reporting, and AI applications.</p><br><p><strong>4. Job Deliverables</strong> - <strong>Accountability</strong></p><br><p><strong>Description</strong></p><br><p>Deliverables</p><br><p>Pipeline Success Rate ≥99%</p><br><p>Data Freshness SLA Meets agreed refresh schedule</p><br><p>Data Quality Score ≥95%</p><br><p>Pipeline Documentation 100%</p><br><p>Incident Response ≤2 hours for pipeline failures</p><br><p><strong>5. Know How (applying of knowledge &amp; skills in job tasks)</strong></p><br><p><strong>Description</strong></p><br><p>Data Pipeline Development</p><br><p>• Design, build, and maintain ETL/ELT pipelines using Azure Data Factory, Synapse, or Databricks.</p><br><p>• Integrate data from D365 ERP, POS, e-commerce, and external sources into the data warehouse.</p><br><p>• Monitor pipeline performance and resolve data failures promptly.</p><br> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>We are looking for a&nbsp;</span><strong>Senior Data Engineer</strong><span>&nbsp;to join us. In this role, you will design, build, and optimize data pipelines and infrastructure to ensure that high-quality, reliable data powers our products, analytics, and decision-making. You will work closely with data scientists, analysts, and software engineers to enable scalable data-driven solutions that support Klivvr’s mission of delivering innovative fintech experiences.</span><br><br><p><b>What you'll do:</b></p><br><ul> <li>Design, develop, and maintain scalable ETL/ELT pipelines and data workflows.</li> <li>Build and optimize data warehouses, data lakes, and real-time streaming systems.</li> <li>Partner with product, engineering, and analytics teams to understand data needs and deliver reliable solutions.</li> <li>Ensure data integrity, governance, security, and compliance across systems.</li> <li>Monitor, troubleshoot, and optimize data infrastructure for performance and cost efficiency.</li> <li>Mentor junior engineers and contribute to best practices and standards across the data team.</li> <li>Stay current with industry trends, tools, and emerging technologies in big data and cloud computing.&nbsp;</li> </ul><br><p><b>To succeed in this role, you'll need to have: </b></p><br><ul> <li>4+ years of experience in data engineering or similar roles.</li> <li>Strong proficiency in SQL and at least one programming language (Python, Scala, or Java).</li> <li>Experience with data pipeline creation, orchestration &amp; data processing frameworks (AirByte, AirFlow, DBT/DataForm, Apache Spark, etc.)</li> <li>Hands-on expertise with cloud platforms.</li> <li>Experience with big data technologies.</li> <li>Knowledge of data modeling, warehousing (BigQuery) and database design.</li> <li>Strong understanding of data governance, security, and compliance.</li> <li>Excellent problem-solving, communication, and collaboration skills.</li> <li>Experience in the fintech is a plus.</li> <li>Familiarity with&nbsp;<strong>machine learning pipelines</strong>&nbsp;and MLOps is a plus.</li> <li>Contribution to open-source data engineering projects.</li> <li>Familiarity with code version control and deployment &amp; monitoring strategies</li> </ul><br> </div>