Description QA ETL Testing We are looking for an experienced
ETL Tester / Data Quality Test Engineer to validate data extraction, transformation, and loading processes and ensure the accuracy, completeness, consistency, and reliability of data across enterprise data platforms.
The ideal candidate will have strong SQL skills, hands-on experience testing ETL/ELT pipelines, and a solid understanding of data warehousing, data integration, and data validation. You will work closely with data engineers, business analysts, and QA teams to identify data issues, validate business rules, and ensure high-quality data delivery for reporting and analytics.
Key Responsibilities- Design, develop, and execute test plans, test cases, and test scenarios for ETL/ELT pipelines and data integration workflows.
- Validate data extraction, transformation, and loading processes across source systems, staging environments, data warehouses, and target systems.
- Perform source-to-target data validation to ensure data accuracy, completeness, consistency, and integrity.
- Write complex SQL queries to verify data transformations, perform reconciliations, and identify data discrepancies.
- Test data mapping documents, transformation logic, business rules, aggregations, calculations, and derived fields.
- Validate data quality across large datasets, including duplicate records, missing values, incorrect formats, null values, and referential integrity issues.
- Test incremental loads, full loads, historical data loads, and change data capture (CDC) processes where applicable.
- Perform backend testing for data warehouses, data marts, and analytical reporting solutions.
- Validate data used in BI reports and dashboards to ensure that reported metrics align with underlying source and business requirements.
- Collaborate with data engineers and developers to investigate defects, identify root causes, and verify fixes.
- Create and maintain test data, test scripts, test evidence, defect reports, and test execution documentation.
- Support regression, integration, system, and user acceptance testing as required.
- Contribute to improving data testing processes, reusable validation scripts, and test automation.
Required Qualifications- 5+ years of experience in ETL testing, data warehouse testing, data validation, or database testing.
- Strong proficiency in SQL, including complex joins, subqueries, CTEs, aggregations, window functions, and data reconciliation queries.
- Hands-on experience testing ETL/ELT pipelines and validating source-to-target data flows.
- Strong understanding of data warehousing concepts, dimensional modeling, fact and dimension tables, and star/snowflake schemas.
- Experience validating data transformations, business rules, data mappings, and data integration logic.
- Familiarity with relational databases such as SQL Server, Oracle, or similar platforms.
- Experience identifying, documenting, tracking, and retesting data-related defects.
- Strong analytical and problem-solving skills, with attention to detail and the ability to investigate complex data issues.
- Ability to work collaboratively with data engineering, QA, analytics, and business teams.
Technical SkillsCandidates should have experience with several of the following technologies:
- SQL and databases: Advanced SQL, SQL Server, Oracle, or comparable relational databases.
- ETL/ELT tools: SSIS, Informatica, Talend, Azure Data Factory, AWS Glue, or similar data integration tools.
- Data platforms: Snowflake, Azure Synapse, Amazon Redshift, or other data warehouse platforms.
- BI and reporting: Power BI, Tableau, or similar reporting tools for validating data and business metrics.
- Test automation: Selenium or Playwright or automated data validation frameworks.
- Test and defect management: Jira, Azure DevOps, or similar tools.
Nice to Have- Experience testing cloud-based data platforms and modern data pipelines.
- Familiarity with data quality frameworks, automated reconciliation, and anomaly detection.
- Experience validating APIs or data feeds that integrate with ETL workflows.
- Knowledge of CI/CD practices and automated testing within data engineering environments.
- Experience testing large-scale datasets and distributed processing systems.
- Familiarity with Agile/Scrum methodologies and standard QA processes.
- Experience in banking, financial services, payments, or other data-intensive industries.
Requirementsno need to work Rao
