Test Automation Engineering Manager

Good-to-Have Skills:

• Experience with data governance tools such as

  Apache Atlas

  ,

  Collibra

  , or

  Alation

• Understanding of

  DataOps

  methodologies and practices
• Familiarity with

  monitoring/observability tools

  such as

  Datadog

  ,

  Prometheus

  , or

  CloudWatch

• 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:

• Strong hands-on experience with Data Quality (DQ) framework design and automation
• Expertise in PySpark, Python, and SQL for data validations
• Solid understanding of ETL/ELT pipeline testing in Databricks or Apache Spark environments
• Experience validating structured and semi-structured data formats (e.g., Parquet, JSON, Avro)
• Deep familiarity with AWS data services: S3, Glue, Athena, Lake Formation, Data Catalog
• Integration of test automation with AWS Glue Data Catalog or similar catalog tools
• UI automation using Selenium with Python for dashboard and web interface validation
• API testing using Postman, Python, or custom API test scripts
• Hands-on testing of BI tools such as Tableau, Power BI, Looker, or custom visualization layers
• CI/CD test integration with tools like Jenkins, GitHub Actions, or GitLab CI
• Familiarity with containerized environments (e.g., Docker, AWS ECS/EKS)
• Knowledge of data classification, access control validation, and PII/PHI tagging
• Understanding of data governance standards (e.g., GDPR, HIPAA, CCPA)
• Understanding Data Structures: Knowledge of various data structures and their applications.
• Ability to analyze data and identify inconsistencies.
• Proven hands-on experience in test automation and data automation using Databricks and AWS.
• Strong knowledge of Data Integrity Frameworks (DIF) and Data Quality (DQ) principles.
• Familiarity with automated testing frameworks like Selenium, JUnit, TestNG, or PyTest.
• Strong understanding of data transformation techniques and logic.