Data Engineer

About us

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.

Your role

As a Data Engineer, you will play a central role in the development and operation of the University's modern data platform. You will design, build, and maintain scalable data pipelines that support analytics, reporting, and data-driven decision-making. Working closely with analysts, data architects, and business stakeholders, you will ensure high-quality, secure, and efficient data integration from diverse sources into our Azure-based data ecosystem.

Key Responsibilities:

• Design and implement data pipelines using Azure Data Factory and Databricks
• Develop scalable and maintainable ETL/ELT workflows to ingest data from various systems (e.g., SAP, APIs, databases)
• Ensure data quality, consistency, and lineage across all stages of the data lifecycle
• Optimize pipeline performance and resource usage in line with Azure best practices
• Collaborate with data analysts, integration experts, and business stakeholders to understand data requirements and translate them into technical solutions
• Support the implementation of data governance, security, and compliance policies
• Document data models, workflows, and technical architecture
• Monitor data flows and troubleshoot issues in production environments
• Contribute to the continuous improvement of the university's data engineering capabilities and platform strategy

Your profile

• Degree in Computer Science, Data Engineering, Information Systems, or equivalent professional experience
• Hands-on experience with Azure Data Factory and Databricks, including PySpark or Spark SQL
• Strong understanding of data modeling, data warehousing, and ETL/ELT processes
• Familiarity with Delta Lake and working in cloud-native data environments
• Knowledge of data governance, access control, and security concepts in Microsoft Azure
• Experience with version control, CI/CD practices, and agile development workflows
• Exposure to business intelligence tools (e.g., Pow