Senior QA Engineer (Automation, Data & ETL Testing)

Job Description – Senior QA Engineer (Automation, Data & ETL Testing) Position: Senior Automation Engineer – Data & ETL Testing Experience: 5–8 Years About the Role Infoya is looking for an experienced Senior Automation Engineer with strong expertise in test automation , database testing, ETL validation, and data pipeline testing. The ideal candidate will have hands-on experience working with cloud-based data platforms, complex data architectures, and business intelligence solutions. The candidate will be responsible for ensuring data accuracy, integrity, consistency, reliability, and performance across enterprise data pipelines and analytical platforms. Key Responsibilities 1. Test Automation (Mandatory) Design, develop, and maintain automated testing frameworks for database, ETL, and data pipeline validation. Develop reusable automated test scripts using Python, SQL, or equivalent technologies. Automate source-to-target reconciliation, data quality checks, schema validation, and regression testing. Integrate automated tests into CI/CD pipelines. Improve test coverage, execution efficiency, and overall QA productivity through automation 2. Database Testing & Data Validation Design and execute comprehensive database testing strategies for complex enterprise applications. Perform advanced SQL-based validation, including complex joins, aggregations, stored procedures, and data reconciliation. Validate data integrity, completeness, accuracy, and consistency across source and target systems. Conduct backend testing using PostgreSQL and StarRocks. Identify data quality issues, transformation defects, and performance bottlenecks. 3. ETL & Data Pipeline Testing Validate end-to-end ETL/ELT workflows, including data extraction, transformation, loading, and reconciliation. Test batch and streaming data pipelines for accuracy, completeness, and reliability. Validate transformation rules, data mappings, business logic, and aggregation calculations. Perform incremental load, full load, duplicate detection, and data consistency testing. Verify error handling, recovery mechanisms, and data pipeline dependencies. 4. Cloud & Data Platform Testing Work with Google Cloud Platform (GCP) to validate cloud-based data processing and storage solutions. Perform data validation across PostgreSQL, StarRocks, and other enterprise data platforms. Test data ingestion, processing, transformation, and integration workflows. Collaborate with Data Engineers, Cloud Engineers, and Developers to troubleshoot data-related issues. 5. Business Intelligence & Reporting Validation Validate Tableau dashboards, reports, KPIs, and analytical outputs. Reconcile dashboard data against underlying databases and source systems. Verify business rules, filters, aggregations, calculations, and reporting accuracy. Ensure consistency between analytical data layers and business reporting. 6. Performance Testing (Good to have knowledge) Perform database query performance testing and optimization validation. Evaluate ETL pipeline throughput, processing latency, and scalability. Support load, stress, and volume testing for data-intensive applications. Identify performance bottlenecks and collaborate with engineering teams on improvements. Required Qualifications 5-8 years of experience in Software Quality Engineering, with significant hands-on experience in database and ETL testing. Strong experience in developing automated test scripts and reusable testing frameworks. Excellent SQL skills with expertise in complex queries, joins, aggregations, and data validation. Practical experience with PostgreSQL, StarRocks, Tableau, and GCP. Knowledge with GCP services such as BigQuery, Cloud Storage, Dataflow, or Cloud Composer. Proven experience validating end-to-end data pipelines and ETL/ELT processes. Strong understanding of data warehousing concepts, dimensional modeling, and data quality principles. Experience working in Agile delivery environments. Strong analytical, troubleshooting, communication, and stakeholder collaboration skills. Preferred / Good-to-Have Skills Performance testing experience for databases and large-scale data pipelines. Familiarity with PyTest, Great Expectations, dbt testing, or similar data quality automation tools. Experience testing large-volume datasets and near-real-time data processing. Exposure to DevOps, CI/CD, and cloud-native testing practices. Expected Outcomes The successful candidate will: Establish robust automated testing coverage across critical data pipelines. Improve data quality and reduce production data defects. Strengthen end-to-end validation across source systems, databases, and Tableau reporting. Accelerate regression testing through automation and reusable frameworks. Collaborate proactively with engineering teams to improve the reliability and scalability of enterprise data platforms. Ideal Candidate: A technically strong, hands-on Senior QA Engineer who combines advanced SQL and database testing expertise with automation-first thinking, ETL/data pipeline validation experience, and practical exposure to modern cloud data platforms.