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Metro location and mid-level experience increase competition, but niche data-QA and smaller employer moderate applicant density.
Core data QA skills are transferable, but capital-markets and post-trade expertise increases domain specificity.
Explicit 5–7 years plus mandatory Python, PySpark, SQL, automation, and data-quality requirements.
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Lead data validation and regression testing for capital-markets data pipelines across legacy and modern platforms.
Develop and maintain Python/SQL/PySpark-based automation frameworks for data quality assurance and integrate these into CI/CD pipelines.
Collaborate with engineering and business teams to embed data quality controls and resolve data anomalies across batch, cloud, and post-trade systems.
5-7 years of experience in Data QA, Data Engineering QA, or Python-based automation roles.
Strong skills in Python, SQL, and working knowledge of PySpark for data validation and automation framework development.
Experience with data validation across ETL/ELT pipelines and integration of automated tests into CI/CD environments using tools like Jenkins, GitLab, or Azure DevOps.
Location requirement: ITPP-Kharadi, Pune, Maharashtra, India.
Experience operating in capital markets domain, especially post-trade, market data, or reference data validation contexts.
Proven ability to build reusable automated data validation scripts and regression test packs for complex data environments.
Comfortable working with legacy and modern platforms, cloud-based data lakes, batch orchestration tools (e.g., AutoSys), and embedding QA in agile/DevOps delivery models.