Overview

Primary Skill: ETL Testing

Responsibilities

• Design, develop, and execute ETL test scenarios, test cases, and test scripts based on business and technical requirements.
• Perform source-to-target data validation across databases and data platforms.
• Validate ETL mappings, transformations, joins, filters, aggregations, lookups, calculations, and business rules.
• Perform database testing using complex SQL queries, joins, subqueries, CTEs, stored procedures, and aggregate functions.
• Validate data across source, staging, transformation, and target layers.
• Perform data reconciliation to ensure data completeness, accuracy, consistency, and integrity.
• Validate record counts, duplicate records, NULL values, data types, constraints, referential integrity, and business rules.
• Identify data truncation, mapping, transformation, and data quality issues.
• Validate ETL batch jobs, including execution status, failure handling, restart/recovery scenarios, and dependencies.
• Test full, incremental, delta, and historical loads, including SCD Type 1 and Type 2 implementations.
• Validate data migration and data conversion activities where applicable.
• Analyze ETL failures and collaborate with developers and data engineers to identify root causes and resolve defects.
• Prepare test data and execute positive, negative, boundary, and exception scenarios.
• Log, track, and manage defects using Azure DevOps, Jira, or similar tools.
• Support SIT, regression, integration, and production validation activities.
• Develop and maintain reusable SQL queries and data validation scripts; identify opportunities to automate repetitive validation.
• Participate in Agile ceremonies and collaborate with Business Analysts, Developers, Data Engineers, and QA teams.

Qualifications

• ETL Testing, Data Warehouse Testing, SQL, Source-to-Target Validation, Data Reconciliation, SQL Server
• Job Location: Hyderabad / Bengaluru / Pune / Coimbatore
• About The Job
• We are looking for a hands-on ETL Test Engineer with strong expertise in ETL and Data Warehouse testing. The ideal candidate should be proficient in SQL, source-to-target data validation, transformation testing, data reconciliation, and data quality validation. The role requires independently analyzing data mappings, validating business rules, identifying data discrepancies, and working closely with developers and data engineering teams to resolve defects.
• Know Your Team
• Join our team that invests in your growth. Our Infinity Program empowers you to build your career with role-specific skill development, leveraging immersive learning platforms. You'll have the opportunity to work with some of the best minds serving insurance customers in the US, UK and Canadian markets.
• 5–6 years of overall QA/testing experience, including at least 3 years of hands-on ETL/Data Warehouse testing.
• Strong hands-on experience in ETL testing, Data Warehouse testing, and source-to-target validation.
• Strong understanding of ETL architecture, data flow, transformation logic, and source-to-target mapping.
• Excellent SQL skills, including complex queries, joins, subqueries, CTEs, UNION/UNION ALL, GROUP BY/HAVING, aggregate functions, window functions, CASE statements, and date/string functions.
• Experience with stored procedures, views, data comparison, and reconciliation queries.
• Hands-on experience with SQL Server and database tools such as SQL Server Management Studio or equivalent.
• Experience validating full, incremental, delta, and historical data loads.
• Good understanding of SCD Type 1 and Type 2, data reconciliation, and data quality validation.
• Experience with test and defect management tools such as Azure DevOps or Jira.
• Ability to independently write SQL queries to validate large volumes of data.
• Strong understanding of SDLC, STLC, QA processes, and Agile/Scrum methodologies.
• Ability to trace data across systems, identify discrepancies, and investigate root causes.
• Strong analytical, problem-solving, communication, and collaboration skills.
• Exposure to ETL test automation and data validation automation frameworks.
• Experience creating reusable SQL scripts and automating repetitive data validation activities.
• Familiarity with ETL job monitoring and scheduling tools.
• Experience with Excel for data analysis and reconciliation.
• Exposure to cloud data platforms and modern data engineering environments.
• Experience working with large-volume enterprise datasets.
• People firs - Empower employees to succeed.
• Nurture leaders - Nurture from within.
• Enjoy wins - Recognize and celebrate win.
• Collaboration - Foster a culture of collaboration and people-centricity.
• Diversity - Committed to diversity, equity, and inclusion.