ABOUT THE ROLE Role Description: We are seeking a highly skilled, hands- on Senior QA & Test Automation Engineer , will play a critical role in ensuring the accuracy, reliability, and performance of our enterprise data platforms. Reporting to the Test Automation Engineering Manager , you will work at the intersection of data engineering, quality assurance, and automation to validate complex data flows across pipelines , with a special focus on semantic layer validation and GraphQL API testing . This is a hands-on role that demands deep technical proficiency in both manual and automated data validation . You will be responsible for understanding the business logic behind data transformations, validating the flow of data through various systems, and ensuring that semantic and API layers deliver consistent and contract-compliant outputs. You will actively participate in the design and development of automation frameworks , collaborating closely with the QA Manager and engineering teams to ensure testability and maintainability are built into the system from the start. You will also contribute to test strategy, execution planning, and defect lifecycle management , working across cross-functional teams to maintain high standards for data quality. This role is ideal for someone who is passionate about data quality , has hands-on experience with ETL/ELT pipelines , is comfortable working with cloud-native data platforms (AWS, Databricks, etc.) , and has a strong grasp of testing best practices , including API schema validation, CI/CD integration, and semantic layer testing. Youll have the opportunity to shape and contribute to a modern data quality engineering practice , ensuring that downstream consumers such as analytics teams, business stakeholders, and machine learning models can fully trust the data they rely on. Roles & Responsibilities: Collaborate with the Test Automation 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. Track, manage, and report defects using tools like JIRA , ensuring proper prioritization, root cause analysis, and resolution. Collaborate with Data Engineers, Product Managers, and DevOps teams to integrate tests into CI/CD pipelines and promote shift-left testing practices. Ensure comprehensive test coverage across the data lifecycle, including data ingestion, transformation, delivery, and consumption. Participate actively in QA ceremonies (daily standups, sprint planning, retrospectives), and continuously drive improvements to QA processes and culture. Good-to-Have Skills: Experience with data governance tools such as Apache Atlas, Collibra, or Alation 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 focused on ETL/Data Pipeline Testing in cloud-native environments. Strong in SQL, Python, and optionally PySpark comfortable writing complex queries, automation scripts, and custom data validation logic. Practical experience with manual validation of data pipelines, including source-to-target testing and business rule verification. Proven ability to support, maintain, and enhance automation test suites and contribute to framework improvements in collaboration with QA leadership. Experience validating GraphQL APIs, semantic layers (e.g., Looker, dbt), and ensuring schema/data contract compliance. Familiarity with data platforms and tools such as Databricks, AWS Glue, Redshift, Athena, or BigQuery. Strong understanding of QA methodologies, including test planning, test case design, test data management, and defect lifecycle tracking. Proficiency in tools like JIRA, TestRail, or Zephyr for test case and defect management. Skilled in building and automating data quality checks: schema validation, null checks, duplicates, threshold alerts, and data transformation validation. Hands-on with API testing using tools like Postman, pytest, or custom automation frameworks built in Python. Experience working in Agile/Scrum environments, actively participating in QA ceremonies and sprint cycles. Exposure to integrating automated tests into CI/CD pipelines using tools like GitHub Actions, Jenkins, or GitLab CI. Education and Professional Certifications Bachelors degree in computer science and engineering preferred, other Engineering field is considered; Masters degree and 6+ years experience Or Bachelors degree and 8+ years Soft Skills: Excellent analytical and troubleshooting skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals Strong presentation and public speaking skills.
