Sr. Test Automation Engineer

Roles & Responsibilities:

• Collaborate with the QA Manager to design and implement end-to-end test strategies for data validation, semantic layer testing, and GraphQL API validation.
• Perform manual validation of data pipelines, including source-to-target data mapping, transformation logic, and business rule verification.
• Develop and maintain automated data validation scripts using Python and PySpark for both real-time and batch pipelines.
• Contribute to the design and enhancement of reusable automation frameworks, with components for schema validation, data reconciliation, and anomaly detection.
• Validate semantic layers (e.g., Looker, dbt models) and GraphQL APIs, ensuring data consistency, compliance with contracts, and alignment with business expectations.
• Write and manage test plans, test cases, and test data for structured, semi-structured, and unstructured data.
• Track, manage, and report defects using tools like JIRA, ensuring thorough root cause analysis and timely resolution.
• Collaborate with Data Engineers, Product Managers, and DevOps teams to integrate tests into CI/CD pipelines and enable shift-left testing practices.
• Ensure comprehensive test coverage for all aspects of the data lifecycle, including ingestion, transformation, delivery, and consumption.
• Participate in QA ceremonies (standups, planning, retrospectives) and continuously contribute to improving the QA process and culture.
• Experience building or maintaining test data generators
• Contributions to internal quality dashboards or data observability systems
• Awareness of metadata-driven testing approaches and lineage-based validations
• Experience working with agile Testing methodologies such as Scaled Agile.
• Familiarity with automated testing frameworks like Selenium, JUnit, TestNG, or PyTest.

Must-Have Skills:

• 69 years of experience in QA roles, with at least 3+ years of strong exposure to data pipeline testing and ETL validation.
• Strong in SQL, Python, and optionally PySpark comfortable with writing complex queries and validation scripts.
• Practical experience with manual validation of data pipelines and source-to-target testing.
• Experience in validating GraphQL APIs, semantic layers (Looker, dbt, etc.), and schema/data contract compliance.
• Familiarity with data integration tools and platforms such as Databricks, AWS Glue, Redshift, Athena, or BigQuery.
• Strong understanding of test planning, defect tracking, bug lifecycle management, and QA documentation.
• Experience working in Agile/Scrum environments with standard QA processes.
• Knowledge of test case and defect management tools (e.g., JIRA, TestRail, Zephyr).
• Strong understanding of QA methodologies, test planning, test case design, and defect lifecycle management.
• Deep hands-on expertise in SQL, Python, and PySpark for testing and automating validation.
• Proven experience in manual and automated testing of batch and real-time data pipelines.
• Familiarity with data processing and analytics stacks: Databricks, Spark, AWS (Glue, S3, Athena, Redshift).
• Experience with bug tracking and test management tools like JIRA, TestRail, or Zephyr.
• Ability to troubleshoot data issues independently and collaborate with engineering for root cause analysis.
• Experience integrating automated tests into CI/CD pipelines (e.g., Jenkins, GitHub Actions).
• Experience validating data from various file formats such as JSON, CSV, Parquet, and Avro