ETL QA Engineer

10+ years in data / ETL testing, including strategy ownership on at least one large migration or re-platforming programme

• Proven reconciliation and equivalence testing - demonstrating that a rebuilt pipeline produces output equivalent to a legacy one, and defending that conclusion to stakeholders

• Expert SQL - complex analytical validation queries written independently against unfamiliar schemas Databricks / PySpark for validation at data volume Python for test automation and harness development

• Data quality and test automation frameworks - Great Expectations, dbt tests, pytest or equivalent Test strategy authorship - coverage models, risk-based prioritisation, entry and exit criteria, defect taxonomy multi-target validation across at least two of Databricks, SQL Server, Vertica or comparable

• Experience testing PHI-scoped healthcare data, including de-identified or synthetic test data Defect management and reporting in Azure Boards Team leadership - setting standards and reviewing others’ coverage

• Ability to state an uncomfortable quality position to senior stakeholders and hold it Preferred Experience evaluating LLM or GenAI systems - MLflow, LangSmith, Weights & Biases or equivalent; LLM-as-judge design and its failure modes Applied statistics - sampling design, confidence intervals, significance testing, calibration measurement Epic EHR data experience

• Caboodle or Clarity Clinical coding systems: ICD, CPT, SNOMED, LOINC Multi-tenant data isolation testing Experience testing code- or SQL-generation systems Prior work where test evidence was presented directly to client executives

• Software Design and Best Practices: Apply software design principles such as SOLID and Domain-Driven Design.

• Understand code patterns and practices.

• Maintain currency in technical skills and industry trends.