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Oracle
Role & seniority: Senior QA Engineer / QA Lead (WMS functional/automation focus)
Stack/tools: Selenium, Python; automated test frameworks; CI/CD (Jenkins); AI/ML-driven test generation and analytics tools; custom QA tooling
Design, develop, and maintain test plans, cases, and scenarios for WMS modules (Inbound/Outbound/Inventory/Slotting/Labor)
Lead functional, integration, system, smoke, and regression testing; drive defect management and regression coverage
Build scalable automated test frameworks, integrate tests into CI/CD, mentor junior QA engineers, and advocate best practices
Strong experience in Selenium-based automation with Python
Expertise in QA lifecycle, test design, and defect management
Experience integrating test suites into Jenkins pipelines; cross-functional Agile collaboration
Ability to analyze results, provide actionable quality insights, and improve coverage
Experience with AI/ML tools for test generation, selection, and analytics
Familiarity with WMS domains (Inbound/Outbound/Inventory/Slotting/Labor)
Participation in design/code reviews and testability improvements
Location & work type: Location and work-type not specified in provided text; presumed enterprise/remote-friendly depending on Oracle policies.
Develop and maintain comprehensive test plans, test cases, and test scenarios for WMS functional modules (Inbound, Outbound, Inventory, Slotting, Labor Management). Lead and execute functional, integration, system, smoke, and regression tests to validate WMS workflows and configurations Lead the design, development, and maintenance of scalable, reusable and reliable automated test frameworks and scripts using Selenium and Python and custom tools to improve efficiency. Integrate automated test suites into CI/CD pipelines using Jenkins Identify, log, track, and proactively drive resolution of defects; ensure comprehensive regression coverage Analyze test results to provide actionable insights for product quality improvements Collaborate closely across cross-functional, geographically dispersed Agile teams including developers, business analysts, and product managers in all phases of the SDLC. Mentor and provide technical guidance to junior QA engineers, fostering skill growth and standards adherence. Participate actively in design and code reviews to ensure testability, maintainability, and best coding practices. Continuously improve test coverage, automation efficiency, and process quality through research and adoption of best practices and new tools. Contribute to QA metrics tracking and reporting for management and stakeholders. Leverage AI and machine learning tools to optimize test case selection, reduce manual effort, and accelerate defect detection. Integrate AI-driven test generation tools into the automation pipeline to enhance test suite robustness and adaptability Stay updated on trends in AI for software quality and advocate for the adoption of relevant tools and processes to continuously improve product quality Use AI-based analytics platforms to identify patterns in test results, predict high-risk areas, and recommend test coverage improvements
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