Philadelphia
e&e is seeking a Lead Data QA for a hybrid contract opportunity in Philadelphia, PA! The Lead Data QA is responsible for defining and driving the overall Data Quality Assurance strategy for enterprise-scale data platforms. This role ensures that all data systems meet rigorous standards for accuracy, performance, integration, security, and compliance. The Lead Data QA will provide leadership and mentorship to a team of data QA analysts and testers, establish quality frameworks for ETL/ELT pipelines, and integrate automation within Azure Data Factory (ADF), Databricks, and Snowflake environments. The ideal candidate possesses a deep understanding of data engineering, automation frameworks, and regulatory data compliance (HIPAA, CMS) within modern cloud architectures. Responsibilities: Leadership & Strategy • Define and own the enterprise Data QA strategy encompassing functional, non-functional, integration, and performance testing., • Lead and mentor a distributed team of Data QA professionals across multiple programs and data initiatives., • Establish and maintain data quality SLAs, KPIs, and dashboards for critical datasets., • Collaborate with data governance, engineering, and architecture teams to embed QA best practices across the data lifecycle. Data Testing & Validation • Design and implement automated test plans, scripts, and frameworks for ELT/ETL pipelines., • Validate complex payer datasets including claims, membership, provider, and clinical data., • Conduct FHIR-based API testing for CMS interoperability and compliance standards., • Verify HEDIS measure calculations, healthcare quality metrics, and performance data accuracy., • Log and track defects using appropriate QA tools; provide detailed feedback to engineering and architecture teams. Automation Strategy & Framework • Develop and implement a data QA automation framework for Databricks (Delta Live Tables, Delta constraints) and ADF pipelines., • Utilize Great Expectations for reusable validation suites integrated into CI/CD workflows., • Embed automated schema validation, reconciliation logic, and drift detection into data pipeline operations. CI/CD Integration • Develop QA gates and automated quality checks within Azure DevOps pipelines for Databricks Jobs/DLT, SQL metadata, and ADF deployments., • Collaborate with DevOps and Engineering teams to embed QA automation into continuous integration and deployment processes. Technical Delivery • Partner with ADF, Databricks, and Snowflake teams to ensure end-to-end data quality., • Build and maintain automation frameworks leveraging Python, PySpark, and SQL., • Participate in code reviews, data model validation, and regression testing across environments., • Work with business and data governance teams to identify, investigate, and remediate data quality issues. Performance & Compliance • Design and execute automated load and stress tests for large-scale pipelines and dataflows., • Ensure all data QA processes align with HIPAA, CMS, and payer industry compliance standards., • Support audits through proper documentation of QA processes, test results, and lineage verification. Requirements: Education: • Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field. Experience & Skills: • 10+ years of experience in Data QA/Testing, with at least 5 years in a leadership capacity., • Strong proficiency with Azure Databricks (Delta Lake, Delta Live Tables, Unity Catalog)., • Hands-on experience with Azure Data Factory pipelines, monitoring, and CI/CD deployment., • Advanced skills in Python, PySpark, and SQL for test automation., • Experience with Great Expectations, Azure DevOps, and data quality automation frameworks., • Familiarity with data governance, PII compliance, and enterprise data quality frameworks., • Proven success integrating QA practices into DevOps pipelines within cloud data environments., • Excellent communication, leadership, and cross-functional collaboration abilities., • Experience in Agile/Scrum environments is a plus. Preferred Qualifications: • Experience with HL7/FHIR data models beyond payer use cases., • Knowledge of Lakehouse and medallion architecture, • Familiarity with BI validation using Power BI or Tableau., • Understanding of data governance platforms (e.g., Collibra)., • Prior experience designing data QA automation frameworks for pipelines and regression testing., • Certifications such as Microsoft Certified: Azure Data Engineer Associate or Databricks Certified Data Engineer.