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Senior Quality Assurance Engineer - ETL @ Datavail

INOnsiteFull-timeJob reference 1864
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About this role

Datavail is a leading provider of data management, application development, analytics, and cloud services, with more than 1,000 professionals helping clients build and manage applications and data via a world-class tech-enabled delivery platform and software solutions across all leading technologies. For more than 17 years, Datavail has worked with thousands of companies spanning different industries and sizes, and is an AWS Advanced Tier Consulting Partner, a Microsoft Solutions Partner for Data & AI and Digital & App Innovation (Azure), an Oracle Partner, and a MySQL Partner. Title: Senior ETL Tester with Health Care Location: Mumbai/Hyderabad/Bengaluru Exp: 8+ Years Remote Primary Skills:

• Strong experience in Data QA / ETL Testing (6+ years preferred).

• Hands-on experience with modern cloud data warehouses, such as:

• Snowflake

• BigQuery

• Redshift

• Azure Synapse

• Microsoft Fabric

• Excellent SQL skills (must-have):

• Complex joins, window functions, aggregations

• Query optimization and performance tuning

• Experience testing ETL/ELT pipelines (e.g., using Airflow, DBT, Informatica, etc.).

• Strong understanding of:

• Data warehousing concepts (Star/Snowflake schemas)

• Data lakes and lakehouse architectures

• Experience with API and backend testing is a plus.

• Familiarity with test automation tools or scripting (Python preferred).

• Exposure to CI/CD pipelines and DevOps practices is desirable.

Job Description:

• Design and execute end-to-end data validation strategies for ETL/ELT pipelines.

• Validate data accuracy, completeness, and integrity across cloud data warehouses.

• Develop and optimize complex SQL queries for data validation and analysis.

• Perform functional, regression, and integration testing for data workflows.

• Collaborate with data engineers, analysts, and product teams to understand business rules and data transformations.

• Build and maintain automated test frameworks for data validation.

• Ensure adherence to data quality standards and governance policies.

• Identify defects, track issues, and ensure timely resolution.

• Mentor junior QA engineers and drive QA best practices.

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