Key Responsibilities:
- Validate end-to-end ETL workflows, data transformations, and data movements across multiple systems.
- Perform data quality checks, including completeness, consistency, integrity, duplication, and reconciliation.
- Write and execute complex SQL queries to validate large datasets.
- Conduct source-to-target (S2T) mapping validation and data lineage verification.
- Design, develop, and execute manual and automated data validation test cases.
- Identify, analyze, and document data defects, inconsistencies, and anomalies.
- Collaborate with engineering teams to understand data requirements, schemas, and business rules.
- Test across data warehouses, data lakes, reporting layers, and BI dashboards.
- Support performance testing of ETL jobs to ensure scalability and reliability.
- Participate in CI/CD and automation pipelines for continuous data quality.
- Maintain test coverage documentation, test plans, and QA standards for data validation.

Qualification:




- 3–7 years of experience as a Data QA / ETL Tester / Data Validation Engineer.
- Robust command of SQL, including complex joins, aggregations, window functions, and stored procedures.
- Hands-on experience with ETL testing tools and frameworks (e.g., Informatica, Talend, DataStage, ADF, Glue — as applicable).
- Experience validating data pipelines in cloud platforms (AWS / Azure / GCP preferred).
- Solid understanding of data warehousing concepts (staging, fact/dimension tables, SCDs, partitioning).
- Familiarity with file formats such as CSV, JSON, Parquet, Avro, XML.
- Experience with big data ecosystems (Spark, Hadoop, Databricks) is a plus.
- Knowledge of Python/Scala for automation is an advantage.
- Strong analytical, debugging, and problem-solving skills.
- Hands-on experience in using tools like Jira, Git, Jenkins, Postman, etc.

📌 Data QA Engineer (Pune)
🏢 UTS Global
📍 Pune