We areseekingahighly skilled, hands-on Senior QA & Test Automation Specialist(TestAutomationEngineer)withstrong experience indata validation,ETL testing,test automation, andQA process ownership. This role combines deep technical execution with a solid foundation in QA best practices includingtest planning, defect tracking, and test lifecycle management.
You willbe responsible fordesigning and executing manual and automated test strategiesfor complexreal-time and batch data pipelines, contributing to thedesign of automation frameworks, and ensuring high-quality data delivery across ourAWS and Databricks-based analytics platforms.
The role is highlytechnical and hands-on, with a strong focus onautomation, metadata validation, andensuring data governance practicesare seamlessly integrated into development pipelines.
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.
