Experience:
3 to 5 Years
Experience
- 3 to 5 years of experience in QA automation, with a strong focus on testing data pipelines, ETL/ELT processes, and data quality validation across platforms such as Oracle, SQL Server, and Snowflake.
- Proven experience in leading small teams or mentoring junior engineers.
Technical Skills
- Strong expertise in QA practices for data platforms, including designing automated quality frameworks, validating data pipelines, and risk-based testing.
- Proficiency in automated testing strategies and tooling, with specific languages, frameworks, and CI/CD tools:
- Languages: Python (pandas, sqlalchemy, pytest), SQL (ANSI SQL, T-SQL, PL/SQL), and familiarity with Java/Scala for Spark jobs where applicable.
- Data testing & validation frameworks: Great Expectations, Soda, Amazon Deequ, dbt tests, pytest (with fixtures for data), and custom SQL-based test suites.
- Data platforms & libraries: Snowflake (including Snowpark APIs, Streams, Tasks, Time Travel), Oracle, SQL Server, and experience validating transformations from source → Snowflake.
- ETL/Orchestration tools: Apache Airflow, Dagster, or equivalent job orchestrators; familiarity with tools like Informatica or Talend is a plus.
- CI/CD & Automation: Jenkins, GitLab CI/CD, GitHub Actions, or Azure DevOps for pipeline integration; containerization with Docker and familiarity with Kubernetes for test environments.
- Version control & collaboration: Git, GitHub/GitLab workflows, code review and merge request based validation.
- Test management & monitoring: TestRail for test case management, plus reporting and dashboarding tools (e.g., Allure, Grafana) for data quality metrics and alerts.
- Strong analytical and troubleshooting skills to quickly isolate defects and drive process improvements.
- Effective communication and collaboration skills to work closely with Product Owners, Delivery Coaches, Engineering, and business stakeholders.
We are looking for a detail-oriented and innovative QA Automation Engineer to design, develop, and execute automated testing solutions and comprehensive test plans for Source Systems and Business Data Products. The ideal candidate will have strong expertise in validating the integrity, accuracy, and consistency of enterprise data across multiple business domains.
This role is responsible for ensuring the quality of data processed through ETL pipelines and data modeling scripts across platforms such as Oracle, SQL Server, and Snowflake. The QA Automation Engineer will collaborate closely with data engineering, QA, and business teams to support scalable, reliable, and high-quality data delivery across the organization.Key Responsibilities
- Design and Develop Automated Data Quality Tests:
- Build reusable automated tests to validate data ingestion, transformations, and loading across Oracle, SQL Server, and Snowflake.
- Verify data integrity, completeness, schema conformity, and business logic through all layers — from raw landing zone to curated data products.
- Ensure End-to-End Data Pipeline Validation:
- Automate testing for ETL/ELT processes, including data staging, cleansing, fact and dimension population, and final consumption layers.
- Monitor transformations and data flows across platforms (Oracle → Snowflake, SQL Server → Snowflake, etc.).
- Leverage Snowflake-Specific Testing Capabilities:
- Utilize Snowflake's native SQL features (e.g., streams, tasks, time travel, variant types) in test development.
- Automate regression and functional testing for Snowflake data models, stored procedures, materialized views, and Snowpark-based transformations.
- Build and Maintain a Cross-Platform Test Framework:
- Extend or integrate automation frameworks compatible with Oracle, SQL Server, and Snowflake.
- Apply data validation tools (e.g., Great Expectations, Soda, Deequ) to verify transformation accuracy and data quality rules.
- Utilize TestRail for Test Case Management:
- Design, document, and maintain test cases and test plans in TestRail.
- Track execution, report defects, and ensure traceability across automated and manual test cases.
- Collaborate Across Data Engineering and Modeling Teams:
- Work with data engineers, modelers, and analysts to define test criteria for ingestion jobs, business logic, and reporting outputs.
- Provide early feedback on development tickets and participate in code reviews.
- Support CI/CD and Test Automation in Production Pipelines:
- Integrate automated tests into CI/CD workflows using tools such as Jenkins, GitLab CI/CD, GitHub Actions, or Azure DevOps.
- Ensure pipeline-based test execution for pull requests and releases, containerize test environments (Docker) where appropriate, and validate data integrity during production releases, schema updates, and migrations.
- Develop Monitoring and Alerting for Data Quality:
- Implement anomaly detection and alerting for critical pipelines (e.g., volume drops, schema drift, business rule violations).
- Create dashboards or automated reports for monitoring data quality trends.
- Ensure Test Coverage, Reusability, and Documentation:
- Maintain reusable and version-controlled test suites across platforms.
- Document test strategies, data quality standards, and platform-specific practices.
Skills: qa automation,snowflake,data quality,etl,sql,oracle
