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# Data QA / Test Engineer

- **Company:** Morgan McKinley
- **Location:** London, United Kingdom
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Business Logic, Automation of Tests, Microsoft Azure, Continuous Integration, Data Validation, Data Governance, Python (Programming Language), Sql Optimization, Delivery Pipeline, Pytest, Data Lakes, Pyspark, Data Pipelines, Databricks
- **Published:** November 6, 2026
- **Apply:** https://www.reed.co.uk/jobs/data-qa-test-engineer/57538843

## About the Role

* Experience testing data extracted from or integrated with eFront (eFront Invest / FIA / FrontScript).
 * Proven track record as a Data QA Engineer, Data Quality Engineer, or Data Test Automation Engineer on enterprise cloud migrations.
 * Hands-on testing and reconciliation experience within Azure Databricks / Delta Lake environments.
 * Familiarity with private markets, fund accounting, or private equity financial structures (NAV, cash flows, capital calls, distributions).

If you feel like you could be suitable for this role, do not hesitate and apply ASAP!
Skills

 * QA
 * Test
 * eFront
 * Databricks

## Description

We are seeking a hands-on Senior Data QA / Data Quality Engineer to lead automated migration testing, source-to-target reconciliation, and pipeline validation. You will be responsible for ensuring that all data flowing from eFront lands in Databricks with 100% mathematical and schema integrity., * Source-to-Target Reconciliation: Design, build, and execute automated validation queries to compare legacy eFront extracts against target Databricks Delta Lake tables (Bronze, Silver, Gold).

 * Business Logic & Metrics Testing: Verify complex private equity calculation logic (cash flows, commitments, valuations, NAV, and return metrics) across data pipelines.
 * Automated Data Quality Frameworks: Develop automated test suites using Python (PyTest), PySpark, and advanced SQL to catch discrepancies, nulls, and duplicate records.
 * CI/CD Quality Gates: Integrate automated testing and data validation checks into Azure DevOps (ADO) deployment pipelines.
 * Schema Drift & Anomaly Detection: Monitor and resolve schema evolution, edge-case discrepancies, and late-arriving financial records.