Senior QA Engineer

Role Purpose : We are looking for a technically strong QA Automation Engineer / SDET to build and maintain automated testing solutions for data-intensive applications and systems. The role requires a strong programming foundation in Python, hands-on experience with Pytest, PySpark and SQL, and the ability to understand application code, develop reliable automated tests, and leverage AI agents to improve testing efficiency. This is an automation-first role, with approximately 85–90% of testing expected to be automated and minimal manual testing. The successful candidate will contribute to scalable test frameworks, data validation, automated regression coverage, and AI-enabled testing workflows. Key Responsibilities  - Design, develop and maintain automated test suites, targeting approximately 85–90% automation coverage with minimal reliance on manual testing. - Develop robust and reusable test automation frameworks using Python and Pytest. - Write and maintain automated test cases covering functional, integration, API, regression and data-processing scenarios. - Use PySpark to validate data transformations, processing logic, data quality and expected outputs across data-intensive workflows. - Write SQL queries to validate application data, verify database states, identify inconsistencies and reconcile expected versus actual results. - Integrate Python, Pytest, PySpark and SQL into automated validation workflows wherever appropriate. - Understand application code, test existing implementations, investigate failures and identify defects through code-level analysis. - Design reusable test utilities, fixtures, assertions, test-data management mechanisms and failure-reporting capabilities. - Leverage AI agents to support test-case generation, test automation, code analysis, failure investigation and other relevant QA workflows. - Create, maintain and improve SKILL.md files defining instructions, workflows, tools and reusable capabilities for AI agents. - Evaluate AI-generated test cases and code for correctness, reliability, maintainability and adequate test coverage. - Integrate automated test execution into CI/CD pipelines and support repeatable regression validation. - Collaborate with developers, data engineers and other stakeholders to understand requirements, review code, investigate defects and improve overall software quality. - Identify opportunities to reduce repetitive manual effort through automation, reusable components and AI-assisted workflows. Skills and Experience: - Strong hands-on programming experience in Python, including writing maintainable code, reusable utilities, functions, modules and debugging logic. - Practical experience with Pytest, including test design, fixtures, parametrisation, assertions, test organisation and failure handling. - Hands-on experience with PySpark, including validating distributed data-processing logic, transformations and expected data outputs. - Strong SQL skills, including joins, aggregations, filtering, subqueries and data validation across relational databases. - Experience building and maintaining automated test frameworks rather than relying primarily on manual test execution. - Ability to connect automated test execution with backend or database validation and assert expected results. - Ability to read and understand application code, trace execution logic, investigate failures and distinguish application defects from test issues. - Practical experience leveraging AI agents or AI-assisted development workflows for coding, testing, debugging or task automation. - Ability to write and maintain Markdown-based SKILL.md files for defining reusable AI-agent skills, instructions or workflows. - Experience with version control, preferably Git, and automated execution through CI/CD pipelines. Must Have skills:  - Experience with Selenium or Playwright for UI automation, where relevant to the application. - API automation using Python libraries and tools. - Experience testing data-intensive applications, distributed processing workflows or data platforms. - Experience with automated data-quality checks, reconciliation and database integrity validation. - Familiarity with Docker, Linux environments and CI/CD tooling. - Experience evaluating AI/LLM outputs, validating generated code or building AI-assisted testing workflows. - Experience working in Agile development environments.