Acuity Analytics (the trading name of Acuity Knowledge Partners) is a global, tech-first organization helping financial institutions and corporates make better decisions through research, data, analytics and AI-enabled solutions. We combine deep financial services expertise with strong engineering, digital and AI capabilities to solve complex, real-world problems.

With a team of 7,200+ analysts, data specialists and technologists across 28 locations, we work with more than 800 organizations worldwide to drive efficiency, unlock insight and deliver measurable impact. Our success is built on the strength of our people—by investing in talent, encouraging collaboration and creating room to grow, we enable our teams to do their best work for clients.

Looking for a Data QA Engineer to lead data validation and regression testing across capital-markets data pipelines spanning legacy and contemporary platforms. The role will focus on ensuring data accuracy, completeness, consistency, integrity, and adherence to business rules through source-to-target validation, schema checks, reconciliation, lineage review, and automation-led regression testing.

The candidate will build reusable Python/SQL-based validation frameworks, support batch and cloud execution, validate post-trade data flows, identify data anomalies,



and partner with engineering and business teams to embed strong data-quality controls. The ideal candidate combines data QA expertise, capital-markets domain understanding, automation-first thinking, and strong ownership of data quality across environments.

- Perform source-to-target data validation across legacy and modern systems, including SQL Server and cloud-based data environments.
- Validate data accuracy, completeness, consistency, timeliness, duplication handling, referential integrity, schema structures, metadata, lineage, and business-rule conformance.
- Build and maintain automation-first regression testing frameworks using Python, PySpark, SQL, and reusable validation utilities.
- Execute field-level, dataset-level, transformation-level, and aggregate-level data checks across source, staging, warehouse, data lake, and downstream systems.
- Validate ETL/ELT pipelines, post-trade data flows, stored procedures, batch jobs, and cloud-based execution workflows.
- Design reusable test cases, validation scenarios, reconciliation checks, automated scripts, and regression packs for repeatable data-quality assurance.
- Write, optimize, and troubleshoot complex SQL queries and stored procedures across SQL Server, Oracle, PostgreSQL, or similar platforms.

📌 QA Data Engineer (Maharashtra)
🏢 Acuity Analytics
📍 Maharashtra