• Data QA Lead

    Insight Global (Newtown, PA)


    Job Description

    A leader in the healthcare payor space (Medicare/Medicaid) is hiring for a Data QA Lead to define, build and implement the QA Strategy for their Enterprise Data Office. The team is looking for someone with a deep understanding of healthcare related data (i.e. PHI, FHIR, HEDIS, Payer Data Standards etc.) specifically supporting an Azure Cloud and Databricks platform environment. As the Data QA Lead you will assess the organization's current data lifecycle and projects, then develop a tailored QA strategy to drive automation frameworks and pipeline improvements. Key Responsibilities include:

    Leadership & Strategy

    • Define and own the enterprise Data QA strategy, covering functional, non-functional, integration, security, and performance aspects.

    • Establish data quality SLAs, metrics, and dashboards for business-critical datasets.

    • Set automation strategy for Databricks (Delta Live Tables, Delta constraints) and Azure Data Factory (ADF) pipelines.

    Data Testing & Validation

    • Design and implement test plans and automation for ELT/ETL pipelines.

    • Validate healthcare payer data, FHIR interoperability compliance, and HEDIS quality reporting.

    • Identify, log, and track defects; provide actionable feedback to engineering and architecture teams.

    Automation & Integration

    • Build and maintain QA automation frameworks leveraging PySpark, SQL, and Python.

    • Implement reusable Great Expectations suites integrated with CI/CD workflows.

    • Develop Azure DevOps pipelines with QA gates for Databricks Jobs/DLT, SQL Metadata, and ADF deployments.

    • Enforce Delta table constraints and automate schema validation, drift detection, and reconciliation logic.

    Performance & Scalability

    • Design automated load and stress tests for large-scale data pipelines.

    • Collaborate with engineering teams (ADF, Databricks, Snowflake) to ensure end-to-end data quality.

    Compliance & Governance

    • Ensure QA practices align with HIPAA, CMS, and payer industry standards.

    • Validate data lineage and traceability in partnership with governance teams.

    • Document QA processes, test results, and maintain audit-ready evidence.

     

    We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

    Skills and Requirements

    • 10+ years in Data QA/testing, with at least 5 years in a lead role.

    • Strong expertise in Azure Databricks (Delta Lake, Delta Live Tables, Unity Catalog).

    • Hands-on experience with Azure Data Factory (pipelines, monitoring, CI/CD).

    • Proficiency in Python, PySpark, SQL for test automation.

    • 5+ years working in the healthcare domain with a deep understanding of PHI data, payer data standards, and CMS interoperability

    • 6+ years of experience working in an Azure Cloud based environment

    • Familiarity with Great Expectations, Azure DevOps, and Collibra.

    • Knowledge of data governance, PII compliance, and data quality frameworks.

    • Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field. • Experience with HL7/FHIR data models beyond payer use cases.

    • Experience with Lakehouse architecture and medallion design patterns.

    • Familiarity with BI tools (Power BI, Tableau) to validate reporting layer data.

    • Understanding of data governance platforms (e.g., Collibra).

    • Knowledge of automation frameworks for data QA and pipeline regression testing.

     

    Certifications: Microsoft Certified: Azure Data Engineer Associate, Databricks Certified Data Engineer.