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.
