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, Py. Spark 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 Py. Spark 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 , Py. Spark 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 Py. Spark , 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 .