Qa Engineer

About your role GPS Tech stack and engineering ecosystem needs to evolve at a fast pace to cater to strategic technology drivers and to meet end customer experience demands. There is need for a seasoned technologist leader who can lead / drive this space with dedicated energy, passion and inspiration. The role would focus on services, functional, data test automation and cover for key capability gaps and give this a shape of a programme with clear prioritised / agreed scope and agenda. The role will leverage / guide the existing automation experts to get this prioritised backlog delivered on time. The key outcomes shall be (but not limited to) to create services and functional / non functional automation suite keeping DevOps principle of speed and quality in mind. The selected candidate shall be nothing short of role model technology expert in the Automation space. The role shall be responsible for managing the day to day activities of the team along with tech guidelines, best practices and reference implementations for the existing and new automation assignments. The role shall ensure that other tech experts are well guided / mentored and the work gets delivered as per tech strategy and guidelines. About you This position requires a strong self-starter with a solid technical automation background and influencing skills. The ideal candidate will help evolve the confidence level in working software as desired and assist in the continuous delivery of the product. Conduct continuous testing to add confidence to the product. Strengthen the regression test pack, ensuring it is fully automated in the build pipelines. Add post-deployment and higher environment test cases for smoke and device compatibility checks. Work closely with the product team and developers in the early phases of story writing and influence as required. Closely monitor build status and assist in root cause analysis when relevant. Build knowledge around AWS cloud to understand underlying components and increase the level of example scenarios for testing based on that. Qualifications & Experience : 2-5 years of experience working as an automation QA engineer. Expertise in designing and implementing automation strategies. Relevant experience with application development tools and frameworks like Maven, Ant, Checkstyle, PMD, Fortify, JUnit, FitNsse, SOAP UI, etc. Experience using source code management tools, e.G., SVN / GitHub. Rich experience in engineering skills, CI / CD, and build / deployment automation tools. Familiarity with TDD, Three Amigos, and Specification by Example in an agile environment. Experience in implementing automated tests in JavaScript / TypeScript, Java using tools such as Gauge, Cucumber, SoapUI, TestComplete. Good understanding of database concepts, such as data models, schemas, tables, queries, views, stored procedures, triggers, indexes, constraints, and transactions, and SQL. Understanding of tools like DBUnit, Selenium WebDriver, Apache JMeter, Liquibase, Postman will be an added advantage. Hands-on experience with build pipelines and the ability to amend the build process. Experience with Jenkins is a plus. Experience in testing non-functional requirements and exploratory testing. Knowledge of development methodologies such as Scrum, Agile, and Kanban. Experience in contract and pact testing, along with stubbing and mocking (virtualization), will be an added advantage. Exposure to performance testing and defining workloads and strategies to test solutions. Familiarity with AWS services, especially serverless architecture and lambdas, is also nice to have. Key Differentiators for Team Members : GenAI Proficiency : Experience with AI coding assistants, copilots, tooling, generative code, and documentation. Utilize GenAI for prototyping, automated testing, documentation, and intelligent code reviews. Observability First Approach : Implement metrics, tracing, and logs as code, with real-time dashboards for every service. Continuous Improvement : Conduct data-driven retrospectives powered by AI insights. Contract Driven Mindset : Enforce strict API contracts and versioning to enable parallel development and autonomous team delivery. Limitless Innovation Mindset : View legacy systems as launchpads for new solutions, challenge assumptions, and pioneer innovations. Code as Craft : Write clean, modular, future-proof code with a focus on transforming old platforms into next-gen systems. Self-Healing Systems : Build automated anomaly detection and remediation for platforms to recover without human intervention. Continuous Learning & Knowledge Sharing : Lead by example, host internal tech talks, publish open-source patterns, and mentor peers. Agile at Scale : Deliver incremental value rapidly, learn from feedback loops, and iterate without sacrificing stability. Legacy Modernization Mindset : Break down monoliths into modular microservices, delivering business value in small slices. Customer-Centric Engineering : Ground decisions in improving user outcomes, reducing friction, and accelerating time to value.