Sr. Test Automation Engineer

highly skilled, hands-on Senior QA & Test Automation Specialist

designing and executing manual and automated test strategies

technical and hands-on

Roles & Responsibilities:

• Collaborate with the QA Manager to design and implement end-to-end test strategies for data validation, semantic layer testing, andGraphQLAPI validation.
• Perform manual validation of data pipelines, including source-to-target data mapping, transformation logic, and business rule verification.
• Develop andmaintainautomated data validation scripts using Python andPySparkfor 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,dbtmodels) andGraphQLAPIs, ensuring data consistency, compliance with contracts, and alignment with business expectations.
• Write and manage test plans, test cases, and test data for structured, semi-structured, and unstructured data.
• Track, manage, and report defects using tools like JIRA, ensuring thorough root cause analysis andtimelyresolution.
• Collaborate with Data Engineers, Product Managers, and DevOps teams to integrate tests into CI/CD pipelines and enable shift-left testing practices.
• Ensure comprehensive test coverage for all aspects of the data lifecycle, including ingestion, transformation, delivery, and consumption.
• Participate in QA ceremonies (standups, planning, retrospectives) and continuously contribute to improving the QA process and culture.
• Experience building ormaintainingtest data generators
• Contributions tointernal quality dashboardsordata observability systems
• Awareness ofmetadata-driven testing approachesandlineage-based validations
• Experience working with agileTestingmethodologies such as Scaled Agile.
• Familiarity with automated testing frameworks like Selenium, JUnit, TestNG, orPyTest.

Must-Have Skills:

• 6-9years of experience in QA roles, with at least3+ yearsofstrong exposure todata pipeline testingandETL validation.
• Strong in SQL, Python, and optionallyPySpark- comfortable with writing complex queries and validation scripts.
• Practical experience with manual validation of data pipelines and source-to-target testing.
• Experience in validatingGraphQLAPIs, semantic layers (Looker,dbt, etc.), and schema/data contract compliance.
• Familiarity with data integration tools and platforms such as Databricks, AWS Glue, Redshift, Athena, orBigQuery.
• Strong understanding of test planning, defect tracking, bug lifecycle management, and QA documentation.
• Experience working in Agile/Scrum environments with standard QA processes.
• Knowledge of test case and defect management tools (e.g., JIRA, TestRail, Zephyr).
• Strong understanding ofQA methodologies, test planning, test case design, anddefect lifecycle management.
• Deep hands-onexpertiseinSQL,Python, andPySparkfor testing and automating validation.
• Proven experience inmanual and automated testingofbatch and real-time data pipelines.
• Familiarity withdata processing and analytics stacks: Databricks, Spark, AWS (Glue, S3, Athena, Redshift).
• Experience withbug tracking and test management toolslikeJIRA,TestRail, or Zephyr.
• Ability to troubleshoot data issues independently and collaborate with engineering for root cause analysis.
• Experience integrating automated tests intoCI/CD pipelines(e.g., Jenkins, GitHub Actions).
• Experiencevalidatingdata from various file formats such asJSON,CSV,Parquet, andAvro
• Strong ability tovalidateand automatedata quality checks: schema validation, null checks, duplicates, thresholds, and transformation validation
• Hands-on experience withAPI testingusingPostman,pytest, or custom automation scripts

Good-to-Have Skills:

• Experience with data governance tools such as Apache Atlas, Collibra, or Alation
• Familiarity with monitoring/observability tools such as Datadog, Prometheus,or CloudWatch

Educationand Professional Certifications

• Bachelor s/Mastersdegree 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.