Data Quality Engineer

Role & Responsibilities

• Validate ETL pipelines, data migrations, and data transformations.
• Perform data validation, reconciliation, completeness, and integrity checks.
• Write complex SQL queries for large-scale data validation.
• Develop Python automation scripts/frameworks for ETL testing.
• Automate regression and data quality validation.
• Integrate test automation into CI/CD pipelines (Jenkins/Azure DevOps).
• Validate cloud-based data pipelines on AWS/Azure.
• Debug ETL failures using Unix/Linux logs.
• Collaborate with developers, data engineers, and product teams in an Agile environment.
• Perform root cause analysis and ensure high data quality.

Preferred Candidate Profile

• 58 years of experience in ETL/Data Warehouse Testing.
• Strong hands-on experience with SQL and Python.
• Experience in ETL validation, data reconciliation, and data quality testing.
• Exposure to AWS or Azure data platforms.
• Experience with CI/CD tools (Jenkins, Azure DevOps) and Git.
• Good understanding of ETL processes and data warehousing concepts.
• Knowledge of Unix/Linux for troubleshooting.
• Experience in the Retail domain is a strong plus.
• Experience with Hadoop, Spark, Databricks, Jira, or TestRail is an added advantage.
• Strong analytical, communication, and problem-solving skills.