Quality Automation Engineer (Database Testing) (Kolkata)

Role Overview We are seeking a highly skilled Automation Engineer to drive end-to-end QA automation across data pipelines, workflow systems, and backend services. The ideal candidate will have hands-on expertise in automation frameworks, API and data validation, and AI-assisted automation. This role requires robust problem-solving skills and the ability to build scalable, reliable automation for complex, data-driven platforms. Required Skills & Experience - 4to 8 years of QA automation experience in enterprise-scale, data-driven systems - Strong proficiency in automation frameworks (e.g., Playwright, Selenium, Cypress, Robot Framework, TestNG/JUnit) - Proficiency in programming languages such as Java / Python / Kotlin for automation development - Strong hands-on experience with API testing and backend validation - Strong SQL skills with ability to: - write complex queries (joins, aggregations) - validate data transformations - reconcile datasets and detect anomalies - Hands-on experience with data platforms such as: - PostgreSQL, Snowflake, Databricks (or similar) - Experience working with workflow orchestration systems (e.g., Airflow), including: - validation of DAG execution - handling failures, retries, and data dependencies - Exposure to cloud and data services (e.g., AWS Glue, Kafka or similar event-driven systems) - Experience with CI/CD tools (Jenkins, GitHub Actions, Azure DevOps) - Exposure to BI/visualization tools (e.g., Power BI) for QA reporting and observability dashboards - Familiarity with AI-assisted automation approaches (e.g., test generation, intelligent test maintenance) - Strong analytical and debugging skills with ability to work with logs, pipeline failures, and distributed systems - Excellent communication and collaboration skills to work across cross-functional teams Preferred Qualifications - Experience in data pipeline validation and ETL/ELT testing - Exposure to hybrid automation architectures (API + data validation + limited UI where required) - Familiarity with test management tools (TestRail, Jira, Confluence) - Experience validating - Event-driven systems (Kafka or similar) - large-scale workflow ecosystems (e.g., Airflow at scale) - Experience building QA or data observability dashboards using Power BI or similar tools - Exposure to AI/ML-driven QA solutions or agentic automation workflows - Exposure to enterprise planning or business systems (e.g., Anaplan, PLM, or similar) is a plus Role Overview We are seeking a highly skilled Automation Engineer to drive end-to-end QA automation across data pipelines, workflow systems, and backend services. The ideal candidate will have hands-on expertise in automation frameworks, API and data validation, and AI-assisted automation. This role requires robust problem-solving skills and the ability to build scalable, reliable automation for complex, data-driven platforms. Required Skills & Experience - 4to 8 years of QA automation experience in enterprise-scale, data-driven systems - Strong proficiency in automation frameworks (e.g., Playwright, Selenium, Cypress, Robot Framework, TestNG/JUnit) - Proficiency in programming languages such as Java / Python / Kotlin for automation development - Strong hands-on experience with API testing and backend validation - Strong SQL skills with ability to: - write complex queries (joins, aggregations) - validate data transformations - reconcile datasets and detect anomalies - Hands-on experience with data platforms such as: - PostgreSQL, Snowflake, Databricks (or similar) - Experience working with workflow orchestration systems (e.g., Airflow), including: - validation of DAG execution - handling failures, retries, and data dependencies - Exposure to cloud and data services (e.g., AWS Glue, Kafka or similar event-driven systems) - Experience with CI/CD tools (Jenkins, GitHub Actions, Azure DevOps) - Exposure to BI/visualization tools (e.g., Power BI) for QA reporting and observability dashboards - Familiarity with AI-assisted automation approaches (e.g., test generation, intelligent test maintenance) - Strong analytical and debugging skills with ability to work with logs, pipeline failures, and distributed systems - Excellent communication and collaboration skills to work across cross-functional teams Preferred Qualifications - Experience in data pipeline validation and ETL/ELT testing - Exposure to hybrid automation architectures (API + data validation + limited UI where required) - Familiarity with test management tools (TestRail, Jira, Confluence) - Experience validating - Event-driven systems (Kafka or similar) - large-scale workflow ecosystems (e.g., Airflow at scale) - Experience building QA or data observability dashboards using Power BI or similar tools - Exposure to AI/ML-driven QA solutions or agentic automation workflows - Exposure to enterprise planning or business systems (e.g., Anaplan, PLM, or similar) is a plus