QA lead

Data Quality Strategy & LeadershipDefine and execute a comprehensive data quality testing strategy for data products, data services, and integrations. Establish best practices for data validation, reconciliation, and anomaly detection. Develop data quality KPIs and metrics to measure and improve data reliability. Lead and mentor a team of data testers and QA engineers to drive excellence in data testing.End-to-End Data Testing & AutomationOversee functional, integration, regression, and performance testing of data pipelines and ETL processes. Implement automated data validation frameworks using Python, SQL, and cloud-native tools. Drive the integration of test automation into CI/CD/CT pipelines for continuous data quality assurance. Define and enforce data testing standards across teams to ensure consistency and accuracy.Integration & Performance TestingLead data API and service integration testing to validate data flows between systems. Conduct performance and scalability testing to ensure the efficiency of data pipelines and queries. Collaborate with data engineers, architects, and DevOps teams to optimize data processing workflows.Governance, Compliance & Issue ResolutionEnsure compliance with regulatory and security standards (e.g., HIPAA, GDPR etc). Establish and maintain data lineage, metadata validation, and data governance controls. Manage and drive resolution of data quality issues, defects, and anomalies through proactive monitoring. Act as a liaison between Product leaders, Delivery Leaders / Technical managers, End Users and QA Testers to ensure alignment on data quality goals.Required Work Experience: Lead and drive the end-to-end data quality strategy for our enterprise data products and services. Required: 15+ years of experience in data quality testing, data validation, or data engineering QA, with at least 3+ years in a leadership role. Expertise in data warehouse, data lake, and ETL testing on cloud-based platforms (preferably GCP BigQuery). Strong proficiency in SQL and Python for data validation and automation. Experience with data testing frameworks (e.g., Great Expectations, dbt tests, Deequ). Proven track record of test automation, CI/CD integration, and performance testing. Defining Test Data requirements / Test Bed for large projects / programs. Strong analytical and problem-solving skills with the ability to debug complex data issues. Excellent leadership, communication, and stakeholder management skills. Preferred: Experience in healthcare data platforms and compliance regulations (HIPAA, FHIR, HL7). Familiarity with data observability, metadata management, and governance tools (e.g., Collibra, Monte Carlo). Knowledge of GCP data services, including Dataflow, Pub/Sub, and Cloud Storage. Experience in data API testing and service validation.