ETL Tester
Job Description
We are seeking an experienced ETL Tester to ensure the quality, accuracy, and reliability of data across our data warehouse, data lake, and analytics platforms. The ideal candidate will have strong hands-on experience in ETL testing, SQL, data validation, and automation of data quality checks. This role will focus on validating data pipelines, source-to-target mappings, transformation logic, and reporting layers to ensure trustworthy data for business decision-making.
The candidate will collaborate closely with Data Engineers, ETL Developers, Business Analysts, Data Architects, and Product Owners to drive data quality throughout the data lifecycle.
Key Responsibilities
ETL & Data Warehouse Testing
- Analyze business requirements, source-to-target mapping (STTM) documents, and data models to design comprehensive test strategies
- Design, develop, and execute test cases for ETL processes, data pipelines, and data warehouse solutions
- Validate data extraction, transformation, and loading logic across source, staging, and target systems
- Perform data reconciliation, row counts, aggregations, and field-level validations
- Validate slowly changing dimensions (SCD), historical data loads, incremental loads, and CDC (Change Data Capture) scenarios
- Test data quality dimensions: completeness, accuracy, consistency, uniqueness, integrity, and timeliness
SQL & Data Validation
- Write complex SQL queries to validate large datasets across multiple databases and platforms
- Perform source-to-target data comparison, duplicate detection, and referential integrity checks
- Validate business rules, transformation logic, derived columns, and data lineage
- Analyze data anomalies, root causes, and work with developers on resolution
Automation & Quality Engineering
- Develop and maintain automated data validation scripts using tools such as Python, PyTest, or ETL testing frameworks
- Automate regression testing for ETL pipelines and reusable data quality checks
- Integrate automated data tests into CI/CD pipelines (Jenkins, Azure DevOps, GitHub Actions)
- Build reusable test assets, utilities, and accelerators for data testing
BI & Reporting Validation
- Validate reports and dashboards built on tools such as Power BI, Tableau, or Qlik
- Verify KPIs, metrics, drill-downs, filters, and aggregations against source data
- Perform end-to-end validation from source systems through data warehouse to reporting layer
Defect Management & Reporting
- Log, track, and manage defects using tools such as Jira, Azure DevOps, or HP ALM
- Perform root cause analysis and collaborate with developers on defect resolution
- Communicate test progress, quality metrics, and risks to stakeholders
- Prepare test summary reports, data quality scorecards, and sign-off documentation
Collaboration & Stakeholder Management
- Participate in requirement reviews, data model walkthroughs, and design discussions
- Work closely with Data Engineers, ETL Developers, Data Analysts, and Business Users
- Contribute to QE best practices, standards, and reusable data testing playbooks
- Support UAT, production validation, and post-deployment data checks
Experience
- 3–8 years of experience in ETL testing, data warehouse testing, or data quality assurance
- Hands-on experience testing large-scale data pipelines and analytics platforms
- Experience working in Agile/Scrum delivery environments
Technical Skills
- Strong expertise in SQL and relational databases (Oracle, SQL Server, PostgreSQL, MySQL, Teradata)
- Hands-on experience with ETL tools such as Informatica, Talend, SSIS, DataStage, Ab Initio, or Azure Data Factory
- Experience testing data warehouse solutions (Snowflake, Redshift, Synapse, BigQuery, Teradata)
- Understanding of data modeling concepts (Star Schema, Snowflake Schema, Fact/Dimension tables, SCD types)
- Familiarity with BI/reporting tools such as Power BI, Tableau, or Qlik
- Knowledge of test management tools such as Azure DevOps (ADO), Jira, or HP ALM
- Experience with CI/CD pipelines and DevOps practices
Preferred Skills
- Hands-on experience with Python or PySpark for data validation and automation
- Familiarity with cloud data platforms (Azure, AWS, GCP)
- Exposure to data governance, data lineage, and metadata management tools
- Experience with API testing (Postman, REST Assured) for data services
- Knowledge of streaming data testing (Kafka, Event Hub, Kinesis)
- Understanding of data privacy, security, and compliance requirements (GDPR, HIPAA)
Key Competencies
- Strong analytical thinking and problem-solving
- Attention to detail and quality-first mindset
- Excellent SQL and data analysis skills
- Strong communication and stakeholder management
- Ability to work with large, complex datasets
- Collaboration and teamwork
- Continuous learning and adaptability to emerging data technologies
Ownership and accountability for data quality outcomes
