Client: Our client is a global organization focused on skilled education and certification.

- Project overview: 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.
- Position overview: 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.
- Technology stack: 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
- 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 Snowflake, PostgreSQL, and ETL tools or equivalent data platforms.
- 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.
- Nice to have: Experience with Python and Pandas for data processing and quality analysis

📌 Senior Data Quality Engineer (Hyderabad-based) (Bengaluru)
🏢 DataArt
📍 Bengaluru