Senior Data Engineer

Role Name: Senior Data Engineer Location: Antwerp, Belgium Remote Work: Yes (Hybrid) Start Date: 25/08/2026 End Date: 15/07/2027 Language Requirement: Dutch at CEFR C2 level

B. Main Responsibilities

Data Pipeline Engineering

• Design, build, optimise, and maintain scalable data pipelines.
• Develop reliable data-processing workflows for large data volumes.
• Integrate data from multiple source systems.
• Build reusable and maintainable data-processing components.
• Monitor and improve pipeline performance and reliability.

Microsoft Fabric Development

• Develop solutions using Microsoft Fabric.
• Work with Fabric pipelines, dataflows, and notebooks.
• Build and maintain modern analytics and data-processing workloads.
• Support scalable ingestion, transformation, and processing patterns.
• Optimise Fabric-based data solutions for performance and maintainability.

Data Integration

• Integrate relational databases, APIs, files, applications, and other data sources.
• Design robust ingestion and transformation processes.
• Develop reusable data-integration patterns.
• Validate and monitor source-to-target data flows.
• Ensure reliable and consistent data delivery.

Lakehouse & Data Warehouse Architecture

• Design and maintain cloud-based lakehouse and warehouse solutions.
• Implement medallion architecture using Bronze, Silver, and Gold layers.
• Support modern Azure-based data architectures.
• Contribute to domain-oriented and data-mesh concepts where applicable.
• Ensure scalability, maintainability, and reuse across data products.

Data Modelling

• Translate reporting and analytics needs into data models.
• Develop reliable and reusable analytical datasets.
• Apply dimensional-modelling principles.
• Work with Kimball-based modelling concepts.
• Optimise data structures for reporting and business intelligence.

Python & SQL Engineering

• Develop data-processing logic using Python.
• Use Python for ETL and data-engineering workloads.
• Develop and optimise SQL queries.
• Improve query performance.
• Support database-related analysis and troubleshooting.

Reporting & Analytics Support

• Support Power BI and analytics environments.
• Prepare trusted datasets for reporting.
• Work with reporting specialists to understand analytical requirements.
• Improve the reliability and consistency of reporting data.
• Support self-service and enterprise analytics needs.

Data Governance & Quality

• Contribute to data-governance standards.
• Implement data-quality controls.
• Support metadata and lineage practices.
• Identify and resolve data-quality issues.
• Promote consistent data-management practices across the platform.

CI/CD & DataOps

• Support CI/CD for data solutions.
• Use version control for data engineering artefacts.
• Automate deployments across environments.
• Improve release and deployment processes.
• Apply modern DataOps and engineering practices.

Documentation & Collaboration

• Document pipelines, dataflows, data models, and a

C. Required Skills & Expertise

Must Have

• Minimum 5 years of experience as a Data Engineer.
• Experience with cloud-based data architectures on Azure.
• Experience with lakehouse or data-warehouse concepts.
• Strong Microsoft Fabric experience, specifically:

• Pipelines
• Dataflows
• Notebooks

• Experience integrating data from multiple source systems.
• Experience supporting reporting and analytics environments.
• Minimum 3 years of Python experience within a data-engineering context.
• Experience with Python, Java, or Scala for data flows and ETL processes.
• Strong SQL knowledge.
• Query optimisation experience.
• Dutch language proficiency at CEFR C2 level.

Should Have

• Experience within a public-sector, municipal, or complex enterprise environment.
• ETL design and implementation.
• CI/CD for data solutions.
• Version control.
• Automated deployment of data solutions.
• Database experience.
• Medallion architecture.
• Bronze, Silver, and Gold data layers.
• Power BI.
• Spark or Spark-based processing.
• Data governance.
• Data-quality management.
• Data lineage.
• Kimball dimensional modelling.
• Lakehouse architecture.
• Data Mesh.
• Domain-oriented data architecture.