· Experience in data / ETL testing, including strategy ownership on at least one large migration or re-platforming programme
\n· Proven reconciliation and equivalence testing — demonstrating that a rebuilt pipeline produces output equivalent to a legacy one, and defending that conclusion to stakeholders
\n· 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
\n· 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
\n· 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
\n· 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
\n· 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
\n· Software Design and Best Practices: Apply software design principles such as SOLID and Domain-Driven Design.
\n· Understand code patterns and practices.
\n· Maintain currency in technical skills and industry trends.
\nSkills
\nETL, SQL, Azure Databricks, Agentic AI testing, Web testing Any Data Automation tool.
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