Test Automation Engineering Manager

Job Description

Roles & Responsibilities: Design and implement robust data validation frameworks focused on the semantic layer, ensuring accurate data model, schema compliance, and contract adherence across services and platforms. Build and automate end-to-end data pipeline validations across ingestion, transformation, and consumption layers using Databricks, Apache Spark, and AWS services such as S3, Glue, Athena, and Lake Formation. Lead test automation initiatives by developing scalable, modular test frameworks and embedding them into CI/CD pipelines for continuous validation of semantic models, API integrations, and data workflows. Validate GraphQL APIs by testing query/mutation structures, schema compliance, and end-to-end integration accuracy using tools like Postman, Python, and custom test suites. Oversee UI and visualization testing for tools like Tableau, Power BI, and custom front-end dashboards, ensuring consistency with backend data through Selenium with Python and backend validations. Define and drive the overall QA strategy with emphasis on performance, reliability, and semantic data accuracy, while setting up alerting and reporting mechanisms for test failures, schema issues, and data contract violations. Collaborate closely with product managers, data engineers, developers, and DevOps teams to align quality assurance initiatives with business goals and agile release cycles. Actively contribute to architecture and design discussions, ensuring quality and testability are embedded from the earliest stages of development. Mentor and manage QA engineers, fostering a collaborative environment focused on technical excellence, knowledge sharing, and continuous professional growth. Must-Have Skills: Team Leadership Experience is also required. Strong 6+ years of experience in Requested Data Ops/Testing is required 7+ to 12 years of Overall experience is expected in Test Automation. Strong experience in designing and implementing test automation frameworks integrated with CI/CD pipelines. Expertise in validating data pipelines at the syntactic layer, including schema checks, null/duplicate handling, and transformation validation. Hands-on experience with Databricks, Apache Spark, and AWS services (S3, Glue, Athena, Lake Formation). Proficiency in Python, PySpark, and SQL for writing validation scripts and automation logic. Solid understanding of GraphQL APIs, including schema validation and query/mutation testing. Experience with API testing tools like Postman and Python-based test frameworks. Proficient in UI and visualization testing using Selenium with Python, especially for tools like Tableau, Power BI, or custom dashboards. Familiarity with CI/CD tools such as Jenkins, GitHub Actions, or GitLab CI for test orchestration. Ability to implement alerting and reporting for test failures, anomalies, and validation issues. Strong background in defining QA strategies and leading test automation initiatives in data-centric environments. Excellent collaboration and communication skills, with the ability to work closely with cross-functional teams in Agile settings. Mentor and manage QA engineers, fostering a collaborative environment focused on technical excellence, knowledge sharing, and continuous professional growth. Good-to-Have Skills: Experience with data governance tools such as Apache Atlas, Collibra, or Alation Understanding of DataOps methodologies and practices 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. Educationand Professional Certifications Bachelors/Masters degree in computer science and engineering preferred.