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.