Job description

As Sr Data Engineer you will drive the design, deployment, and continuous optimization of scalable data pipelines that ingest and process a wide variety of structured and unstructured data sources into Mondia’s AWS-based data warehouse and data lake.
Implement and maintain reliable data ingestion, transformation, orchestration, monitoring, and CI/CD capabilities across the data platform.
Promote operational excellence through automation, GitLab pipelines, Infrastructure as Code, observability, and robust engineering standards.
Ensure that high-quality, secure, and well-governed data is available for reporting, advanced analytics, and AI- and machine-learning-enabled use cases across Mondia’s business entities.
Social insurance Health insurance for employee and family The company will contribute up to EUR 25 per month towards staff perks Benefit from our performance-based bonus scheme , in line with company policy.
EUR equivalent salaries paid in EGP Responsibilities: Collaborate with data engineers, data analysts, product and web analytics teams, software engineers, and business stakeholders in a collaborative and iterative environment.
Provide technical guidance and contribute to the definition of data architecture, engineering standards, and reusable solutions across Mondia’s data ecosystem.
Design and maintain the data foundations required to deliver reliable internal data products, including well-defined datasets, data sources, dashboards, reports, and analytical solutions.
Contribute as a senior member of the team to the reliable operation, performance, scalability, and cost efficiency of Mondia’s AWS-based data platform, including Amazon Redshift, Athena, AWS Glue, Amazon S3, Step Functions, Lambda, EC2, and related AWS services.
Design, build, test, deploy, monitor, and maintain end-to-end data pipelines and data management solutions using AWS Glue, Python, SQL, Spark/PySpark, and similar cloud-based ETL and data-processing technologies.
Design and implement data models, schemas, tables, and storage structures across Amazon Redshift, Amazon S3, and the wider AWS data platform.
Design and maintain orchestration patterns, workflows, event-driven components, and scheduling solutions using AWS Step Functions, AWS Glue Workflows, Amazon EventBridge, AWS Lambda, and similar technologies.
Promote operational excellence through automation, GitLab CI/CD, Infrastructure as Code, observability, testing, documentation, and consistent engineering practices.
Define, implement, and continuously improve automated data-quality and reliability controls, including checks for completeness, accuracy, consistency, validity, duplication, freshness, and reconciliation.
Maintain appropriate data lineage, metadata, technical documentation, and ownership information to improve transparency, trust, and maintainability.
Use appropriate programming languages, integration patterns, and engineering tools to connect systems and deliver reliable data solutions.
Evaluate technologies based on the use case and adapt as platforms and requirements evolve.
Use approved AI technologies and AI-assisted development tools as part of daily engineering activities to improve productivity, code quality, testing, documentation, troubleshooting, and solution design.
Identify opportunities to integrate AI capabilities into data products where they provide measurable business or operational value.
Communicate technical concepts, results, risks, and recommendations clearly to both technical and non-technical stakeholders across all levels of the organization.
Document architectures, data flows, design decisions, operational procedures, and technical standards, while contributing constructively to technical discussions and engineering decisions.
Comply with Mondia’s policies, procedures, security requirements, and data-governance standards, while supporting the company’s mission, vision, and values.
Perform other reasonable job-related duties and responsibilities as assigned by the direct manager.
Bachelor’s degree in Computer Science/Engineering or Statistics.
Skills & Experience: +3 years of professional experience in Data Engineering or Data Warehousing with an integrative perspective, from management to operations involvement and hands-on experience with cloud architecture and cloud technologies such as AWS, Azure or Google Cloud Platform GCP.
Strong proficiency in Python and SQL; experience with Scala, Java, or Bash/Shell scripting is an advantage.
Solid experience with AWS data services, particularly Amazon Redshift, S3, Glue, Athena, Lambda, Step Functions, and EventBridge.
Strong understanding of data warehouse, data lake, ETL/ELT, data modelling, and cloud data architecture concepts.
Experience building and operating scalable data pipelines using AWS Glue, Spark/PySpark, or similar technologies.
Good understanding of cloud security, identity and access management, data protection, monitoring, and logging.
Experience with APIs, microservices, and event-driven integration patterns.
Working knowledge of GitLab CI/CD, automation, Infrastructure as Code, and modern software engineering practices.
Experience using AI-assisted development tools to improve engineering productivity and quality.
Very good written and spoken English, with strong technical documentation and communication skills.