Data QA Engineer
Summary
Kellton is hiring a Data QA Engineer in Pune (work from office) to validate and reconcile data across source and target systems: writing advanced SQL, testing ETL pipelines and transformations, profiling data, and doing root-cause analysis. 6-9 years of data QA experience required; Python/PySpark, Databricks and cloud data platforms are pluses.
Data Quality Analyst / Data QA Engineer
Experience: 6-9 Years
• Location: Pune Kalyani Nagar
• Work Mode: Work From Office (WFO)
Role Overview
We are looking for experienced Data Quality Analysts / Data QA Engineers with strong analytical skills and expertise in data validation, reconciliation, and data pipeline testing. The ideal candidates will be responsible for ensuring data accuracy, consistency, completeness, and quality across source and target systems.
Key Responsibilities
• Perform data validation and reconciliation across source and target systems to ensure data accuracy and consistency.
• Write and execute advanced SQL queries, including complex joins, subqueries, aggregations, and data comparison queries.
• Conduct ETL testing and validate data pipelines, transformations, and data integration processes.
• Perform data profiling, data quality assessments, and completeness and consistency checks.
• Analyze data discrepancies, investigate issues, and perform root cause analysis.
• Work with large datasets to identify anomalies, inconsistencies, and data integrity issues.
• Validate data transformation logic and ensure adherence to defined business rules.
• Prepare and maintain test scenarios, test cases, validation scripts, and defect reports.
• Support data migration validation and testing activities across dierent platforms and environments.
• Use reporting and data analysis tools to monitor and communicate data quality findings.
Required Skills & Experience
• 6-9 years of experience in Data Quality Analysis, Data QA, Data Testing, or Data Analytics.
• Strong proficiency in advanced SQL and complex query writing.
• Hands-on experience with data validation and reconciliation across source and target systems.
• Experience in ETL testing, data pipeline testing, and data transformation validation.
• Strong understanding of data profiling, data quality analysis, and root cause investigation.
• Ability to work with large datasets and identify data anomalies and inconsistencies.
• Experience with reporting and data analysis tools.
• Strong analytical, problem-solving, and troubleshooting skills.
• Good communication skills and the ability to collaborate with cross-functional teams.
Good to Have
• Knowledge of Python or PySpark for data analysis and validation automation.
• Exposure to Databricks or similar data processing platforms.
• Understanding of cloud-based data platforms such as AWS, Azure, or GCP.
• Experience with data migration testing and validation.
• Familiarity with data warehousing concepts and modern data engineering ecosystems.