The Manager of Quality Engineering and Automation is a hands-on quality engineering leader responsible for helping transform a traditional Quality Assurance organization into a modern Quality Engineering organization. This role embeds quality throughout the SDLC by advancing automation-first practices, AI-assisted testing, continuous testing, and measurable engineering standards that improve delivery speed, reliability, and guest-facing quality. This leader will drive adoption of AI-assisted Quality Engineering, including agentic agents capable of reading user stories, acceptance criteria, code changes, build outputs, deployments, and release plans to generate, execute, and maintain automated tests across smoke, functional, regression, user experience, integration, performance, load, and other testing needs. The role manages quality engineers and automation resources across agile product teams and structured project delivery efforts, ensuring consistent standards, clear reporting, and continuous improvement. This position ensures Wynn's digital platforms and enterprise systems meet high standards for quality, performance, security, reliability, and guest experience. Success Profile (What "Great" Looks Like) 37 QA practices evolve into a modern Quality Engineering organization with stronger automation, earlier testing, and measurable quality outcomes37 AI Quality Engineering assistants are used to generate, execute, maintain, and report on automated tests using stories, acceptance criteria, code, builds, deployments, and release plans37 Nightly automation reports provide clear visibility into test results, quality trends, coverage gaps, performance/load concerns, and release risks37 Manual testing effort is reduced while improving automation coverage, test reliability, and delivery confidence37 Digital platforms and enterprise systems consistently meet Wynn's premium standards for quality, reliability, performance, and guest experience Key Responsibilities Quality Engineering Transformation, Automation & AI Enablement 37 Lead the transition from traditional QA practices to a modern Quality Engineering model focused on prevention, automation, engineering discipline, and continuous feedback37 Implement automation-first testing practices across smoke, functional, API, regression, user experience, integration, performance, load, and release validation activities37 Drive adoption of AI Quality Engineering assistants and agentic testing agents to accelerate test creation, execution, maintenance, and reporting37 Establish practical standards, frameworks, and governance for AI-assisted test automation and quality engineering practices37 Champion shift-left quality, continuous testing, and early defect detection across delivery teams Delivery Model Leadership 37 Manage quality engineering delivery across agile product teams for testing efforts37 Align testing strategy to product risk, system complexity, release cadence, and business criticality37 Partner with engineering, product, DevOps, infrastructure, data, and business teams to ensure testing is planned early and executed consistently37 Ensure appropriate quality gates, release readiness criteria, and reporting are in place for both agile and project-based delivery models AI Quality Engineering Assistants & Agentic Test Automation 37 Help design and operationalize AI Quality Engineering assistants that can interpret multiple engineering inputs, including user stories, acceptance criteria, actual code, build artifacts, deployment schedules, and release plans37 Enable agentic agents to generate, execute, monitor, and maintain automated test suites across smoke, functional, API, regression, user experience, integration, performance, load, and release validation testing37 Establish scheduled automated test execution and reporting capabilities that summarize pass/fail results, defect trends, coverage gaps, performance concerns, and release risks37 Implement self-healing and intelligent automation patterns that reduce maintenance effort and improve test reliability37 Ensure AI-generated tests, recommendations, and reports are explainable, auditable, and validated through appropriate engineering controls Engineering & Delivery Excellence 37 Own quality outcomes across the SDLC, including functional quality, integration reliability, performance, load readiness, security validation, accessibility, and guest experience37 Embed automated testing into CI/CD pipelines and release workflows to provide fast, reliable quality feedback37 Partner with engineering teams to improve testability, observability, code quality, and defect prevention37 Support release readiness through data-driven quality insights, risk assessments, and clear go/no-go recommendations Metrics & Continuous Improvement 37 Define and track quality engineering metrics, including automation coverage, defect escape rate, test stability, execution duration, performance trends, load test results, and AI-assisted testing effectiveness37 Produce clear recurring reporting, including automation summaries, release readiness dashboards, and quality risk insights37 Use data, AI-generated insights, and team retrospectives to continuously improve testing practices, tooling, coverage, and speed Team Leadership & Capability Building 37 Manage, coach, and develop quality engineers and automation engineers supporting digital platforms37 Build team capability in AI-assisted testing, agentic automation, CI/CD testing, performance testing, load testing, test data management, and engineering-led quality practices37 Create a culture of ownership, curiosity, technical excellence, and continuous improvement37 Partner with leaders across engineering, product, operations, and enterprise systems to align quality priorities with business outcomes Governance, Risk & Compliance 37 Establish governance for automated testing, AI-assisted testing, test data, quality gates, release readiness, and production risk assessment37 Ensure AI-generated outputs, test results, and recommendations are traceable to requirements, acceptance criteria, code changes, and release scope37 Partner with security, compliance, and engineering teams to ensure quality practices support auditability, privacy, and regulatory expectations37 Maintain appropriate controls for human review, exception handling, and approval of AI-assisted quality decisions Qualifications 37 A minimum of seven (7) years of experience in software quality, test automation, quality engineering, or software engineering, with two (2) years leading or managing teams37 Demonstrated experience transforming QA practices toward quality engineering, automation-first testing, and shift-left quality37 Hands-on experience with test automation frameworks, CI/CD integration, API testing, regression testing, and release validation37 Experience with performance, load, scalability, and reliability testing practices37 Practical experience applying AI, LLMs, or intelligent automation to testing, quality analysis, test generation, defect detection, or reporting37 Strong understanding of agile delivery, user stories, acceptance criteria, build pipelines, deployment processes, and release management37 Ability to translate technical quality insights into clear business and release risk communication Preferred Experience 37 Experience implementing AI-assisted or agentic testing capabilities in an enterprise environment37 Experience with cloud-based platforms, DevOps toolchains, Azure DevOps, GitHub, CI/CD pipelines, observability tools, and automated reporting37 Hospitality, gaming, or high-touch customer experience environments37 Guest-facing platforms such as web, mobile, contact center, booking, loyalty, or digital service experiences37 Enterprise systems such as PMS, CRS, CRM, contact center, workforce management, or integration platforms37 24/7, high-availability, revenue-impacting environments Additional Information