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# Data Engineer - SQL & ETL

- **Company:** Pauwels Consulting
- **Location:** Anderlecht, Belgium
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** Apache HTTP Server, JIRA, Automation of Tests, Microsoft Azure, Batch Processing, Continuous Integration, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Vault Modeling, Data Warehousing, Github, Python (Programming Language), Power BI, Standard Sql, SAS (Software), SQL Databases, Data Streaming, Azure Data Factory, Git, Data Layers, Gitlab-ci, Software Version Control, Sas Visual Analytics, Docker, Databricks
- **Published:** November 3, 2026
- **Apply:** https://europa.eu/eures/portal/jv-se/jv-details/YzBhYmJjZTUtYzY5OC00ZmE1LTg1NGYtMjkzYTBmOGU3MDkyIDM?lang=en

## About the Role

* You bring 5+ years of experience in data engineering, data warehousing, or business intelligence.
 * You possess 2+ years of experience working in a development team utilizing CI/CD workflows.
 * You have expert-level knowledge of SQL and data modeling methodologies such as Kimball, Data Vault, and SCD.
 * You have experience with ETL/ELT processes and batch processing.
 * You're proficient with version control and CI/CD tools including Git, GitHub Actions, Azure DevOps, or GitLab CI.
 * You possess knowledge of BI tools and semantic layers such as Power BI, SAS Visual Analytics, or Apache Superset.
 * You bring experience with data quality and testing automation using tools like dbt tests.
 * You have experience working with Jira or Azure Boards.
 * You're analytical, accurate, and proactive within complex environments.
 * You are fluent in Dutch or French with functional English and a passive understanding of the other national language.

Nice to Haves

 * Certifications such as DAMA CDMP, Azure Data Engineer, or Databricks.
 * Experience with Python, dbt, Docker, or Azure.
 * Knowledge of SAS 9.4, including Base SAS, Enterprise Guide, and DI Studio.
 * Previous experience working within a public sector organization.

## Description

* Translate architectural standards and business needs into concrete functional and technical requirements for data flows.
 * Define data definitions and KPIs within a semantic layer to ensure consistent reporting across the organization.
 * Implement and automate data quality rules to detect and resolve inconsistencies in completeness and accuracy.
 * Develop and maintain ETL/ELT processes to provide a reliable publication layer for various business intelligence tools.
 * Set up and manage CI/CD pipelines for data models and reports to ensure controlled releases.
 * Monitor batch processes and data loads while ensuring compliance with data governance and security policies.