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.