Senior Data Quality Engineer (Hyderabad-based)

About the Position

We are looking for a Data Quality Engineer with hands-on experience implementing and validating data quality. You will design and run tests, check data as it moves from source systems into analytical datasets, and help correct that data. This position is intended for Hyderabad-based candidates who are available to work from the office.

About the Project

Our client runs a large application landscape and the analytical datasets behind it. Quality for that data is part of delivery: test design, quality metrics, checks on data as it moves from source systems into analytical datasets, and practical help when data has to be corrected. The Data Quality Engineer owns that hands-on work. The role covers test design and execution, integrity checks, quality reporting, and support for data remediation, in partnership with the groups that produce and consume the data.

Responsibilities

• Design, implement, and execute data quality test cases, and report metrics that other groups can use.
• Apply data quality checks on large datasets and assist with data fixes.
• Validate data pipelines end to end, covering movement, transformation, completeness, history, freshness, and compliance.
• Use data models and contracts to assess downstream impact and guide quality coverage.
• Use SQL, Snowflake, PostgreSQL, ETL tools, and AI-assisted development tools to analyze data and author reviewed quality checks.
• Integrate quality checks with CI, using version control such as Git, and work across groups to investigate and resolve data quality issues.
• Provide quality evidence, risks, and readiness input for releases.

Requirements

• Risk-based testing methods and experience reporting meaningful quality metrics.
• Hands-on experience validating large datasets and data pipelines, including diagnosing and remediating quality issues.
• Strong SQL and data-modeling knowledge, including assessing the downstream impact of schema and data-contract changes.
• Experience validating data movement, transformation, completeness, history, freshness, and compliance across source and analytical systems.
• Experience with Python and Pandas for data processing and quality analysis.
• Experience with Snowflake, PostgreSQL, and ETL tools or equivalent data platforms.
• Experience with data regulations (GxP).
• Experience using AI-assisted tools and agents to analyze requirements and author SQL and quality checks, with human review of generated output.
• Experience integrating quality checks with CI and working with version control such as Git.
• Experience collaborating effectively in English across engineering and business groups, and providing quality evidence, risks, and readiness input for releases.

Technologies

SQL, Snowflake, PostgreSQL, extract-transform-load (ETL) flows, logical and physical data models, data quality checks and quality metrics, CI platforms (TeamCity, Jenkins, or equivalent), Git, AI-assisted development tools (Cursor, GitHub Copilot, or equivalent), including agents