Role Overview:

Looking for a Test Lead / Data QA Engineer to drive and oversee data validation, manual testing, and regression testing activities across capital markets data pipelines spanning both legacy and modern platforms.

Key Responsibilities:

- Lead end-to-end test planning, execution, and delivery for data validation and regression testing initiatives.

- Manage and mentor offshore/onshore QA teams, including resource planning and task allocation.

- Drive test governance, including test strategy, defect management, and quality metrics.

- Collaborate with business, engineering, and product teams to define requirements and embed quality controls.

- Perform source-to-target data validation across SQL Server and cloud-based environments.

- Build and maintain automation-first regression testing frameworks using Python, PySpark, and SQL.

- Execute data checks across source, staging, warehouse, and data lake systems.

- Integrate regression tests and quality gates into CI/CD pipelines using Azure DevOps, Jenkins, or GitLab.

Tech Stack:

- Python, PySpark, SQL, SQL Server, Oracle, PostgreSQL, Azure DevOps, Jenkins, GitLab, AutoSys, Control-M, Airflow, JIRA, XRay.

Key Competencies:

- 8-12 years of hands-on experience in Data QA, Data Engineering QA, or Python-based automation.

- Strong stakeholder management and ability to communicate quality metrics to leadership.

- Proven experience in establishing test strategies, governance frameworks, and mentoring distributed teams.

- Solid SQL skills for source-to-target validation and complex query optimization.

- Strong understanding of data quality dimensions (accuracy, completeness, consistency, lineage).

- Proficiency in AI-assisted tools like Claude or GitHub Copilot for test acceleration.

Nice to Have:

- Exposure to Azure, AWS, ADF, containers, Kubernetes, or cloud-based data platforms.

- Capital markets domain exposure (post-trade, market data, reference data).

- Experience with DevSecOps and code quality tools like Terraform, Sonar, or ZAP.