We are looking for an experienced Data QA Engineer to drive data validation and automation testing across large-scale data platforms. The ideal candidate will have strong expertise in Python, SQL, PySpark, ETL Testing, Data Validation, and automation frameworks, along with exposure to AI-assisted tools such as GitHub Copilot or Claude.
Must-Have Skills
- Solid Python coding and automation skills
- SQL querying, database testing, and stored procedure validation
- PySpark data pipeline validation
- ETL/ELT Testing and Data Quality Validation
- Source-to-Target Data Validation
- Automation-first Regression Testing Frameworks
- Experience using AI-assisted tools (GitHub Copilot, Claude, etc.)
- Data Pipeline Testing and Defect Analysis [Data QA Engineer 1 (1) (004).docx]
Key Responsibilities
- Perform end-to-end data validation across enterprise data platforms.
- Build and maintain automated validation and regression testing frameworks.
- Validate ETL/ELT pipelines, data transformations, and business rules.
- Execute reconciliation, schema, metadata, and lineage validation.
- Develop reusable Python and SQL-based test utilities.
- Identify and investigate data quality issues and anomalies.
- Collaborate with engineering teams to ensure high-quality data delivery.
- Support post-trade and capital markets data validation initiatives. [Data QA Engineer 1 (1) (004).docx]