Data Engineer: Build Scalable Pipelines & Data Quality

emagine; Luik; On-site; EUR 60,000 - 90,000; Full time

Job summary

emagine is seeking an experienced Data Engineer to design, build, and optimize cloud data pipelines and products across the organization. You will collaborate with fellow Data Engineers to ensure data quality, reliability, and scalable solutions for long-term maintainability.

You will work on QA-focused data tests, collaborate with stakeholders, and optimize performance while ensuring documentation and knowledge transfer for ongoing support.

Qualifications

• 3+ years building cloud data pipelines with SQL, Python/PySpark, and Airflow.
• Experience implementing idempotent or merge-based ingestion patterns.
• Familiarity with automated testing frameworks for data (Great Expectations, Soda tests).
• Strong collaboration, mentoring, documentation mindset and a willingness to contribute to a clean, maintainable data platform.

Responsibilities

• Capability Focused QA – Implement data quality tests that reflect business rules of the capability (completeness, thresholds, referential checks) as well as technical validation tests.
• Collaboration & Handover – Collaborate with the domain cluster’s Data Engineer(s) or relevant stakeholders, document every step (readme.md, Confluence), and conduct knowledge-transfer sessions before roll-off to ensure long-term sustainability.
• Performance Tuning – Optimize jobs and implement data contracts; identify schema or pipeline improvements that reduce long-term compute and enhance maintainability.
• Issue Resolution – Investigate failed loads or QA breaches quickly and coordinate fixes with relevant stakeholders.

Skills

SQL

Python

PySpark

Airflow

Job description

emagine is seeking an experienced Data Engineer to design, build, and optimize cloud data pipelines and products across the organization. You will collaborate with fellow Data Engineers to ensure data quality, reliability, and scalable solutions for long-term maintainability.

You will work on QA-focused data tests, collaborate with stakeholders, and optimize performance while ensuring documentation and knowledge transfer for ongoing support.