Azure Data Engineer

Randstad
Brussel, Belgium
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source

Tech stack

Microsoft Azure Data Governance Data Infrastructure Extract Transform Load (ETL) Data Warehousing Database Models Software Debugging DevOps Python (Programming Language) Meta-Data Management Microsoft Visio Power BI
+6 more
DataOps Data Streaming Azure Data Factory Data Lakes Pyspark Databricks

Job description

  • Analyze functional requirements and design performant Azure data solutions
  • Implement and maintain ETL pipelines using Azure Data Factory, Mapping Data Flows, and PySpark in Databricks
  • Prepare, execute, and document testing activities for high-reliability data operations
  • Debug, troubleshoot, and fix pipeline issues within daily exploitation runs
  • Support the DWH, Data Lake, and overall data governance frameworks, * Guidance: we believe in the power of human connections, so we’ll assign you a dedicated talent manager who will always have your back
  • Career planning and professional development: our learning and development team is here to help you create your Data Consultant career path
  • Team events & Learning from peers: as a technical expert, you will be part of our chapters: vibrant communities filled with colleagues who are passionate about a particular area of expertise

Requirements

  • Master’s degree in IT or related, with hands-on Azure Cloud Data platform expertise (Databricks, Data Factory, Data Lake Gen2, Synapse, DevOps)
  • Strong background in PySpark, Python, database modeling (Erwin/Visio), metadata management, and Power BI
  • Proven ability to translate complex business requirements into technical specs while interacting with business teams
  • Solution-driven, pragmatic problem-solver with strong communication skills who thrives both independently and in a collaborative team environment

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Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:00 min

Separating dataset creation from low-level software implementation steps

Jan Zawadzki · World Congress 2022

4:32 min

Harnessing Spark with Python using PySpark and Py4J

Ayon Roy · LIVE

2:17 min

Mapping the maturity roadmap for scaled devops adoption

Dominik Krichbaum Dominik Krichbaum · World Congress 2026 Europe

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

3:37 min

Scaling machine learning pipelines from prototypes to petabytes

Julian Joseph · LIVE

3:14 min

Structuring career paths and localized data architectures

Ulrich Wurstbauer +1 · LIVE

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