Lead Data Quality Engineer

We need a senior data quality engineer who knows SQL/Python and Azure/Fabric inside out, and can build automated, secure, and resilient end-to-end data validation into CI/CD pipelines.

What this role is about

You will own the quality strategy across our next-generation data platforms. That means embedding automated validation, lineage checks, and regression testing directly into the delivery pipeline — not bolting them on at the end. You will work across Microsoft Fabric, Azure Integration Services, IDMC, and Tibco EBX, making sure every data flow from raw ingestion to business-ready output is solid, scalable, and built to last.

You will also stress-test our systems — simulating failovers, validating mission-critical pipelines, and making sure things hold up when they are pushed to the limit.

What you will actually do

Bring Agile and DevOps thinking into data integration pipelines, grounded in ITIL v4 and DataOps principles

Plug data quality checks and lineage validation straight into Azure DevOps CI/CD pipelines covering ADF, Informatica, and SAP HANA

Build regression and data comparison frameworks across multi-environment ADF pipelines and Informatica mappings, including snapshot reconciliation for critical flows into SAP HANA

Run resilience tests that simulate real-world failures — connector outages, queue backlogs, timeouts — to confirm business continuity and alerting work as expected

What you bring

ISTQB Advanced Test Manager certification, or equivalent hands-on experience in test management

ITIL v3 or v4 Foundation, or comparable IT service management background

Working knowledge of NIST Cybersecurity Framework and EU DORA

Broad QA leadership across automation, performance testing, and shift-left/shift-right practices

Experience running quality engineering in Agile, product-led teams — ideally in financial services

Practical use of AI/ML techniques for defect detection and analytics-driven assurance

Proven track record testing Microsoft Fabric Medallion Architecture and Azure Data Lake Gen2 environments

Strong hands-on skills in T-SQL, Python, ADF, Power Query (M), XMLA, and DAX for validation purposes

Experience writing complex validation queries, running Data Factory pipelines, and validating tabular models via DAX and XMLA

Background in Microsoft Intelligent Data Platform, including Fabric, Azure Databricks, and Azure DevOps delivery

Power BI and analytics testing experience is a clear advantage