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)QA practices evolve into a modern Quality Engineering organization with stronger automation, earlier testing, and measurable quality outcomesAI Quality Engineering assistants are used to generate, execute, maintain, and report on automated tests using stories, acceptance criteria, code, builds, deployments, and release plansNightly automation reports provide clear visibility into test results, quality trends, coverage gaps, performance/load concerns, and release risksManual testing effort is reduced while improving automation coverage, test reliability, and delivery confidenceDigital platforms and enterprise systems consistently meet Wynn’s premium standards for quality, reliability, performance, and guest experienceKey ResponsibilitiesQuality Engineering Transformation, Automation & AI EnablementLead the transition from traditional QA practices to a modern Quality Engineering model focused on prevention, automation, engineering discipline, and continuous feedbackImplement automation-first testing practices across smoke, functional, API, regression, user experience, integration, performance, load, and release validation activitiesDrive adoption of AI Quality Engineering assistants and agentic testing agents to accelerate test creation, execution, maintenance, and reportingEstablish practical standards, frameworks, and governance for AI-assisted test automation and quality engineering practicesChampion shift-left quality, continuous testing, and early defect detection across delivery teamsDelivery Model LeadershipManage quality engineering delivery across agile product teams for testing effortsAlign testing strategy to product risk, system complexity, release cadence, and business criticalityPartner with engineering, product, DevOps, infrastructure, data, and business teams to ensure testing is planned early and executed consistentlyEnsure appropriate quality gates, release readiness criteria, and reporting are in place for both agile and project-based delivery modelsAI Quality Engineering Assistants & Agentic Test AutomationHelp 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 plansEnable agentic agents to generate, execute, monitor, and maintain automated test suites across smoke, functional, API, regression, user experience, integration, performance, load, and release validation testingEstablish scheduled automated test execution and reporting capabilities that summarize pass/fail results, defect trends, coverage gaps, performance concerns, and release risksImplement self-healing and intelligent automation patterns that reduce maintenance effort and improve test reliabilityEnsure AI-generated tests, recommendations, and reports are explainable, auditable, and validated through appropriate engineering controlsEngineering & Delivery ExcellenceOwn quality outcomes across the SDLC, including functional quality, integration reliability, performance, load readiness, security validation, accessibility, and guest experienceEmbed automated testing into CI/CD pipelines and release workflows to provide fast, reliable quality feedbackPartner with engineering teams to improve testability, observability, code quality, and defect preventionSupport release readiness through data-driven quality insights, risk assessments, and clear go/no-go recommendationsMetrics & Continuous ImprovementDefine and track quality engineering metrics, including automation coverage, defect escape rate, test stability, execution duration, performance trends, load test results, and AI-assisted testing effectivenessProduce clear recurring reporting, including automation summaries, release readiness dashboards, and quality risk insightsUse data, AI-generated insights, and team retrospectives to continuously improve testing practices, tooling, coverage, and speedTeam Leadership & Capability BuildingManage, coach, and develop quality engineers and automation engineers supporting digital platformsBuild team capability in AI-assisted testing, agentic automation, CI/CD testing, performance testing, load testing, test data management, and engineering-led quality practicesCreate a culture of ownership, curiosity, technical excellence, and continuous improvementPartner with leaders across engineering, product, operations, and enterprise systems to align quality priorities with business outcomesGovernance, Risk & ComplianceEstablish governance for automated testing, AI-assisted testing, test data, quality gates, release readiness, and production risk assessmentEnsure AI-generated outputs, test results, and recommendations are traceable to requirements, acceptance criteria, code changes, and release scopePartner with security, compliance, and engineering teams to ensure quality practices support auditability, privacy, and regulatory expectationsMaintain appropriate controls for human review, exception handling, and approval of AI-assisted quality decisions
