Software Development Engineer in Test (SDET)

About the role

The Lead IT QA Analyst is responsible for developing or assisting in the development of functional requirements, testing applications, developing test plans, editing and/or writing test cases and test scripts, and evaluating test results to determine compliance with test plans and established business processes. The individual will interact with business development, system developers, end users, and all other groups involved in project development, testing, or test planning. The ideal candidate has strong leadership skills, experience automated and manual testing and a keen eye for detail. The Lead IT QA Analyst is also expected to demonstrate hands-on exposure to AI-assisted software development and testing, leveraging tools such as GitHub Copilot and other generative AI platforms to enhance productivity, quality, and efficiency across the QA lifecycle. QA Activities Actively participate in elaboration sessions to understand requirements. Design and develop comprehensive test strategies and approaches for user stories and defects. Create detailed test scenarios and test cases aligned with acceptance criteria. Write SQL queries for test data setup and validation of test results. Collaborate with Product Owners and Business Analysts to clarify acceptance criteria and obtain sign-off on test cases. Execute test cases, manage defects, and ensure timely resolution through re-testing and regression. Conduct feature demonstrations and walkthroughs. Perform non-functional testing including security, performance, load, stress, multitenancy, and disaster recovery. Document defects clearly with reproducible steps and expected outcomes. Coordinate with the automation team to identify test cases for automation and contribute to script development. Execute end-to-end testing across integrated systems and domains. Collaborate with offshore QA teams for task hand-offs, including participation in off-hours calls when necessary. Support various testing phases such as regression, integration, smoke, and platform testing. Identify opportunities where AI can reduce manual effort and improve test cycle efficiency. Lead adoption of AI-first QA practices within the team and mentor QA engineers on effective use of Copilot and related tools. Collaborate with development and DevOps teams to embed AI-assisted testing practices across the SDLC. Measure and report on productivity and quality gains enabled by AI adoption. Lead Activities Provide leadership across the QA lifecycle, including test planning, execution, defect tracking, and reporting. Guide and oversee the design, implementation, and execution of test cases. Assess team capabilities and allocate tasks to leverage individual strengths. Coordinate onshore and offshore QA efforts, including off-hours collaboration as needed. Monitor defect status and ensure timely re-testing and closure. Adapt to shifting priorities and support agile delivery timelines. Collaborate with Scrum Masters to track sprint progress using key QA metrics. Promote early quality practices by integrating QA into the initial stages of development. Define strategies for non-functional testing and ensure their execution. AI-Driven QA Leadership Leverage GitHub Copilot and AI tools to accelerate test case design, automation scripting, and documentation activities. Drive adoption of AI-assisted testing practices, enabling faster test creation, improved defect detection, and enhanced test coverage. Utilize AI to generate test scenarios, test data, and automation scripts, ensuring inclusion of edge cases and negative paths. Validate and refine AI-generated outputs to ensure accuracy, completeness, and compliance with enterprise standards. Encourage use of shared prompt libraries and reusable AI patterns to standardize QA processes across teams. Identify opportunities to reduce manual effort and improve QA efficiency using AI-driven solutions. Apply AI techniques for defect analysis, root cause identification, and test optimization. Ensure adherence to AI governance, security, and compliance guidelines, including data protection and human validation. Track and report productivity and quality metrics derived from AI adoption. Automation, Frameworks & Continuous Improvement Advocate for and implement test automation to enhance regression coverage and reduce risk. Architect and enhance automated test frameworks and lead automation initiatives. Integrate AI capabilities into test automation frameworks and CI/CD pipelines to improve scalability and efficiency. Integrate automation hooks into the development codebase for seamless testing. Work closely with development staff to design automation hooks into main code base. Research, suggest, and implement tools (including AI-enabled tools) to enhance test efficiency and effectiveness. Collaboration & Delivery Work with release management to track code deployments and test environment readiness. Collaborate with developers, pro

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Company Intel

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