We're looking for 8–12 years of hands-on experience in Data QA, Data Engineering QA, or Python-based automation roles and proven experience leading QA teams, test planning, estimation, execution, defect management, and end-to-end test delivery across complex data programs.

Please share an updated profile to [email protected]

(+91)74839 25904

Mode of Work:Hybrid

Job Location:Gurugram,Pune,Bangalore

Strong stakeholder management skills with the ability to communicate test status, risks, quality metrics, and release readiness to business and technology leadership.

Experience establishing test strategies, governance frameworks, quality standards, resource planning, and mentoring distributed QA teams.

Demonstrated ability to drive cross-functional collaboration, manage competing priorities, mitigate delivery risks, and ensure successful project outcomes in Agile environments.

Strong Python skills for data validation, automation framework development, regression testing, and pipeline verification.

Working knowledge of PySpark for validating large-scale data pipelines and distributed datasets.

Solid SQL skills for source-to-target validation, reconciliation, complex queries, stored procedures, schema checks, and defect analysis.

Experience in data QA, ETL/ELT testing, data pipeline testing, and regression testing across legacy and contemporary platforms.· Proven experience building automation-first regression frameworks, reusable validation scripts, test cases, and data verification utilities.

Strong understanding of data quality dimensions including accuracy, completeness, consistency, timeliness, uniqueness,referential integrity,



and business-rule conformance.

Experience validating metadata, schema structures, data types, constraints, lineage, transformation logic, and downstream outputs.

Exposure to CI/CD tools such as Azure DevOps, Jenkins, or GitLab, with the ability to integrate automated tests and quality gates into deployment pipelines.

Familiarity with batch orchestration tools such as AutoSys, Control-M, or Airflow.

Good analytical skills to understand post-trade, financial, market data, or reference data and translate them into repeatable validation scenarios.

Experience with JIRA, XRay, agile delivery practices, defect management, and QA process governance.

Proficiency in leveraging AI-assisted tools such as Claude or GitHub Copilot to accelerate test case generation, SQL development, data validation, defect analysis, documentation, and test data creation. · Strong communication, problem-solving, attention to detail, and cross-functional collaboration skills. Nice to Have · Exposure to Azure, AWS, ADF, containers, Kubernetes, or cloud-based data platforms. · Experience with relational and NoSQL databases such as SQL Server, MongoDB, Cassandra, DynamoDB, or similar platforms. · Exposure to Hadoop, Spark, Databricks, cloud data lakes, and Big Data validation. · Experience validating batch, streaming, real-time, and Kafka-based ingestion pipelines. · Capital markets domain exposure, especially post-trade, market data, reference data, trade data, or ION/blotter platform integrations. · Exposure to DevSecOps, infrastructure as code, and code quality tools such as Terraform, Bicep, Sonar, Mend, or ZAP.

📌 Data QA Lead (Bengaluru)
🏢 Simpliigence
📍 Bengaluru