Senior Data Engineer

Editx; Antwerpen; Sur place; EUR 85 000 - 120 000; Plein temps

Avantages offerts par ce poste

Hybrid work model

Résumé du poste

Editx in Antwerp, Belgium is seeking a Senior Data Engineer to design, build and maintain scalable data pipelines and analytics workloads across the data platform.

You will develop and optimise data integration, lakehouse and data-warehousing solutions using Python, SQL and Power BI, collaborate on Fabric-based workloads, and ensure reliable data delivery for reporting and decision-making in a hybrid environment.

Qualifications

• Proven experience designing and implementing data pipelines.
• Strong Python and SQL coding skills for ETL/ELT workflows.
• Experience with data modelling concepts and BI storytelling.

Responsabilités

• 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.
• Develop solutions using Microsoft Fabric and Azure-based architectures.
• Support Power BI reporting and analytics environments.
• Implement data-governance and quality practices.

Connaissances

Data engineering

Python

SQL

Data modelling

Power BI

Outils

Microsoft Fabric

Azure

Git

Description du poste

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.