Manager Quality Assurance (IT)

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 Wynns 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 outcomes
• AI Quality Engineering assistants are used to generate execute maintain and report on automated tests using stories acceptance criteria code builds deployments and release plans
• Nightly automation reports provide clear visibility into test results quality trends coverage gaps performance/load concerns and release risks
• Manual testing effort is reduced while improving automation coverage test reliability and delivery confidence
• Digital platforms and enterprise systems consistently meet Wynns premium standards for quality reliability performance and guest experience

Key Responsibilities

Quality Engineering Transformation Automation & AI Enablement

• Lead the transition from traditional QA practices to a modern Quality Engineering model focused on prevention automation engineering discipline and continuous feedback
• Implement automation-first testing practices across smoke functional API regression user experience integration performance load and release validation activities
• Drive adoption of AI Quality Engineering assistants and agentic testing agents to accelerate test creation execution maintenance and reporting
• Establish practical standards frameworks and governance for AI-assisted test automation and quality engineering practices
• Champion shift-left quality continuous testing and early defect detection across delivery teams

Delivery Model Leadership

• Manage quality engineering delivery across agile product teams for testing efforts
• Align testing strategy to product risk system complexity release cadence and business criticality
• Partner with engineering product DevOps infrastructure data and business teams to ensure testing is planned early and executed consistently
• 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

• 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 plans
• 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 testing
• Establish scheduled automated test execution and reporting capabilities that summarize pass/fail results defect trends coverage gaps performance concerns and release risks
• Implement self-healing and intelligent automation patterns that reduce maintenance effort and improve test reliability
• Ensure AI-generated tests recommendations and reports are explainable auditable and validated through appropriate engineering controls

Engineering & Delivery Excellence

• Own quality outcomes across the SDLC including functional quality integration reliability performance load readiness security validation accessibility and guest experience
• Embed automated testing into CI/CD pipelines and release workflows to provide fast reliable quality feedback
• Partner with engineering teams to improve testability observability code quality and defect prevention
• Support release readiness through data-driven quality insights risk assessments and clear go/no-go recommendations

Metrics & Continuous Improvement

• 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 effectiveness
• Produce clear recurring reporting including automation summaries release readiness dashboards and quality risk insights
• Use data AI-generated insights and team retrospectives to continuously improve testing practices tooling coverage and speed

Team Leadership & Capability Building

• Manage coach and develop quality engineers and automation engineers supporting digital platforms
• Build team capability in AI-assisted testing agentic automation CI/CD testing performance testing load testing test data management and engineering-led quality practices
• Create a culture of ownership curiosity technical excellence and continuous improvement
• Partner with leaders across engineering product operations and enterprise systems to align quality priorities with business outcomes

Governance Risk & Compliance

• Establish governance for automated testing AI-assisted testing test data quality gates release readiness and production risk assessment
• Ensure AI-generated outputs test results and recommendations are traceable to requirements acceptance criteria code changes and release scope
• Partner with security compliance and engineering teams to ensure quality practices support auditability privacy and regulatory expectations
• Maintain appropriate controls for human review exception handling and approval of AI-assisted quality decisions

Qualifications :

• 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 teams
• Demonstrated experience transforming QA practices toward quality engineering automation-first testing and shift-left quality
• Hands-on experience with test automation frameworks CI/CD integration API testing regression testing and release validation
• Experience with performance load scalability and reliability testing practices
• Practical experience applying AI LLMs or intelligent automation to testing quality analysis test generation defect detection or reporting
• Strong understanding of agile delivery user stories acceptance criteria build pipelines deployment processes and release management
• Ability to translate technical quality insights into clear business and release risk communication

Preferred Experience

• Experience implementing AI-assisted or agentic testing capabilities in an enterprise environment
• Experience with cloud-based platforms DevOps toolchains Azure DevOps GitHub CI/CD pipelines observability tools and automated reporting
• Hospitality gaming or high-touch customer experience environments
• Guest-facing platforms such as web mobile contact center booking loyalty or digital service experiences
• Enterprise systems such as PMS CRS CRM contact center workforce management or integration platforms
• 24/7 high-availability revenue-impacting environments

Additional Information :

Wynn Resorts is an equal opportunity employer committed to hiring a diverse workforce and sustaining an inclusive culture. Wynn Resorts does not discriminate on the basis of disability veteran status or any other basis protected under federal state or local laws confidential according to EEO guidelines.

Remote Work :

No

Employment Type :

Full-time