Design, develop, and maintain automated test frameworks for data pipelines and ETL workflows.
Build reusable test components for Snowflake, Databricks, ADF, Airflow, streaming pipelines, and more.
Automate schema validation, regression testing, backfill testing, and data contract checks.
Implement automated monitors for stream quality, lag, CDC correctness, and consistency.
Create, execute, and maintain detailed test cases, test plans, and regression suites for data enhancements.
Validate data accuracy, completeness, and integrity across ingestion, transformation, and downstream layers.
Develop SQL-based validation scripts to compare source vs. target datasets.
Perform root-cause analysis for data inconsistencies and work closely with engineering teams to resolve issues.
Partner with Data Engineers, Analysts, and Product teams to understand transformation logic and business rules.
Participate in sprint ceremonies including requirement reviews, estimations, and deployment validations.
Monitor pipeline execution, identify defects early, and verify fixes through structured retesting.
Drive QA best practices across multiple data environments.

6-10+ years of QA Automation experience in data-intensive or analytics-driven environments.
Strong proficiency in SQL for data validation, profiling, and regression testing.
Hands-on experience with Python for automation scripting and data validation.
Extensive experience testing ETL/ELT pipelines and cloud-based data workflows.
Deep understanding of QA methodologies, test planning, defect lifecycle management, and automation principles.
Experience with CI/CD pipelines, Git-based workflows, and automated execution environments.
Strong analytical and troubleshooting skills for identifying data quality issues.
Excellent written and verbal communication skills.
Hands-on experience with Snowflake and at least one test automation framework/tool.

QA Automation Engineer, Data

Key skills