Walk-in || Etl Testing Engineer

Role & responsibilities

Role: ETL Testing & Data Validation Engineer

Experience: 5+ years of relevant experience.

Role Overview:
We are seeking a highly motivated and detail-oriented ETL Testing & Data Validation Engineer to join our Data Engineering and Quality Assurance team. The ideal candidate will be responsible for validating data extraction, transformation and loading (ETL) processes, ensuring the accuracy, completeness, consistency and integrity of data across enterprise data platforms, data warehouses and reporting systems.

The candidate should possess strong Analytical skills, hands-on experience in ETL testing, Advanced SQL expertise and a solid understanding of Data Warehousing concepts.

Key Responsibilities:

1. Requirements Analysis: Analyze business requirements, functional specifications and Source-to-Target Mapping (STTM) documents to define test scope and criteria.

2. Test Planning & Execution: Design, develop and execute comprehensive ETL test plans, test scenarios, test cases and test scripts.

3. ETL & Data Pipeline Validation: Validate data extraction, transformation and loading (ETL) processes to ensure compliance with business rules, data mappings and technical specifications.

4. Data Reconciliation: Perform source-to-target data validation and reconciliation to ensure data accuracy, completeness, consistency, referential integrity and compliance with business rules across multiple databases, applications and data platforms.

5. Backend Testing & Querying: Execute advanced SQL queries to perform backend data validation, data profiling and root cause analysis.

6. Data Warehouse Verification: Validate data warehouse and data integration solutions, including fact and dimension models, aggregations, Slowly Changing Dimensions (SCDs), full-load and incremental-load processes and Change Data Capture (CDC) mechanisms to ensure data accuracy and integrity.

7. Test Data Management: Develop, maintain and manage comprehensive test data sets covering positive, negative, boundary and exception scenarios to support ETL testing, data migration, data reconciliation and end-to-end data validation activities.

8. Automation: Design and implement automated data validation, reconciliation and quality assurance frameworks wherever feasible.

9. Defect Management: Identify, track, prioritize and manage defects through resolution using defect management tools.

10. Cross-Functional Collaboration: Collaborate with Data Engineers, Business Analysts, Developers and business stakeholders to investigate and resolve data-related issues.

11. Test Cycle Support: Support System Integration Testing (SIT), Regression Testing, User Acceptance Testing (UAT) and Production Validation activities. Participate in requirement reviews and provide input on testability, data quality risks and validation strategies.

12. Documentation & Reporting: Create and maintain test execution reports, defect metrics and testing documentation.

13. Mentorship & Best Practices: Contribute to continuous improvement initiatives focused on test automation, data quality monitoring and process optimization. Mentor junior team members and promote best practices in ETL testing, data validation and quality assurance.

Required Skills & Qualifications:

1. Education: Bachelors degree in computer science, Information Technology, Engineering or a related quantitative field.

2. Experience: 5+ years of relevant hands-on experience in ETL testing, data warehouse testing, or database quality assurance.

3. SQL Proficiency: Advanced SQL skills (subqueries, window functions, CTEs, complex joins, stored procedures, performance tuning).

4. Data Warehousing Knowledge: Strong understanding of dimensional modeling (Star/Snowflake schema), OLAP concepts, normalization/denormalization and data lineage.

5. ETL Tool Familiarity: Hands-on experience working with or Testing pipelines built with tools such as Informatica/ Talend/ SSIS, Azure Data Factory (ADF), AWS Glue or dbt.

6. RDBMS & Data Platforms: Strong experience with relational databases (e.g., PostgreSQL, Oracle, SQL Server) and modern Cloud Data Warehouses (e.g., BigQuery, Snowflake, Databricks.)

7. Defect & Test Management: Proficiency with tools such as Jira, Confluence, HP ALM/Quality Center or Zephyr.

Preferred candidate profile