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