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Senior Quality Assurance Lead

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Senior Quality Assurance Lead

Role: Senior QA Lead – Data Pipelines (ETL) + Test Automation Duration: ASAP-End of 2026 Location: Belgium(Hybrid) Languages: English Context: This role is not “join an existing process.” This is come in, take charge, and build it. We need a very experienced tester who can own quality for data pipelines, set standards,build automation, and then grow/lead a small tester team as demand increases. What You’ll Do: ● Take full ownership of testing for our data pipelines (ETL/ELT). ● Create a practical test approach: what we test, how we test it, and what becomes automated. ● Write and automate SQL-based validations (counts, duplicates, null checks, reconciliation, business rules). ● Build a Pytest automation framework for data testing (not just a few scripts). ● Validate Excel outputs using Pandas + OpenPyXL (headers, sheets, values,comparisons). ● Validate XML outputs using lxml + xmlschema (XSD compliance + data rules). ● Own API testing with Postman, automate with Newman. ● Put everything into GitHub Actions so tests run on PRs/merges and give fast feedback. ● Make failures easy to debug: good logs, clear diffs, simple reports. ● Work closely with data engineers: raise issues early, help them reproduce quickly,improve testability. What We Expect From a Senior Person in This Role: ● You can operate without hand-holding. There’s no existing QA setup to “follow.” ● You can make decisions and move: tooling, structure, test priorities, coverage, CI gates. ● You can defend quality with facts (queries, evidence, expected vs actual). ● You can coach others and run a small team (task split, review work, keep standards consistent). ● You’ve done this before: built a test practice from scratch or been the person everyone relied on. Must-Have Skills: ● Strong experience testing data pipelines / ETL (this is the core of the role). ● Strong SQL (you should be comfortable debugging data issues using SQL). Hands-on automation using: ● Pytest ● Pandas + OpenPyXL ● xml / xmlschema ● Postman + Newman ● GitHub Actions ● Databricks ● You write clean, maintainable test code (fixtures, reusable utilities, structure). Good to Have ● Any exposure to cloud data stacks / orchestration tools ● Data quality tooling (Great Expectations etc.) ● Performance / volume testing experience What Success Looks Like After a few months, we should have: ● A working automated regression suite for the critical pipelines ● CI runs in GitHub Actions with clear pass/fail signals ● Repeatable validation checks (SQL + Python) instead of manual effort ● A sensible plan for scaling QA and adding more testers when needed

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