Job Description
· Experience in data / ETL testing, including strategy ownership on at least one large migration or re-platforming programme
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· Proven reconciliation and equivalence testing — demonstrating that a rebuilt pipeline produces output equivalent to a legacy one, and defending that conclusion to stakeholders
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· 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
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· Data quality and test automation frameworks — Great Expectations, dbt tests, pytest or equivalent Test strategy authorship — coverage models, risk-based prioritization, entry and exit criteria, defect taxonomy multi-target validation across at least two of Databricks, SQL Server, Vertica or comparable
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· 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
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· 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
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· 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
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· Software Design and Best Practices: Apply software design principles such as SOLID and Domain-Driven Design.
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· Understand code patterns and practices.
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· Maintain currency in technical skills and industry trends.
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Skills
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ETL, SQL, Azure Databricks, Agentic AI testing, Web testing Any Data Automation tool.