Test Automation Engineering Manager

ABOUT THE ROLE

Role Description:

We are looking for a technically adept and hands-on Test Automation Engineering Manager with strong experience in data quality, data integration, and a strong focus on consumption layer validation. This role blends technical leadership and strong ownership of automated data testing solutions with team leadership responsibilities, ensuring that the data infrastructure across platforms remains accurate, reliable, and high performing.

As a leader in the QA and Data Engineering space, you will be responsible for building robust automated testing frameworks, validating PySpark,

Driving the teams technical excellence. Your work will ensure that all data flows, transformations, and API interactions meet enterprise-grade quality standards across the data lifecycle.

You will be responsible for the end-to-end design and development of test automation frameworks, working collaboratively with your team. As the delivery owner for test automation, your primary responsibilities will include designing and implementing end-to-end test automation frameworks for semantic layer testing, API validation, and schema compliance, ensuring alignment with data quality, performance, and integration reliability standards.

You will also work closely with data engineers, product teams, and platform architects to validate data contracts and integration logic, support the integrity and trustworthiness of enterprise data solutions.

This is a highly technical and hands-on role, with strong emphasis on automation, data workflow validation, and the seamless integration of testing practices into CI/CD pipelines.

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.
• 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.
• 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.
• Proficiency in Python, PySpark, and SQL for writing validation scripts and automation logic.
• Exposure of GraphQL APIs, including schema validation and query/mutation testing.
• 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.

Education and Professional Certifications

• Bachelors/Masters degree in computer science and engineering preferred.

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.