Manager - Quality Assurance (IT)

Wynn Resorts•Las Vegas, NV

About The Position

The Manager of Quality Engineering and Automation is a hands-on quality engineering leader responsible for transforming a traditional Quality Assurance organization into a modern Quality Engineering organization. The role embeds quality throughout the SDLC by advancing automation-first practices, AI-assisted testing, continuous testing, and measurable engineering standards to improve delivery speed, reliability, and guest-facing quality. The manager leads adoption of AI-assisted Quality Engineering, including agentic agents that interpret user stories, acceptance criteria, code changes, build outputs, deployments, and release plans to generate, execute, and maintain automated tests. The position manages quality engineers and automation resources across agile product teams and structured project delivery efforts, establishing consistent standards, reporting, and continuous improvement. It ensures Wynn’s digital platforms and enterprise systems meet standards for quality, performance, security, reliability, and guest experience. The role also owns quality outcomes across the SDLC, builds team capabilities, embeds automated testing into CI/CD and release workflows, and establishes governance and controls for AI-assisted testing and quality decisions.

Requirements

At least seven years of experience in software quality, test automation, Quality Engineering, or software engineering.
At least two years of experience 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.

Nice To Haves

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.
Experience in hospitality, gaming, or high-touch customer experience environments.
Experience with guest-facing platforms such as web, mobile, contact center, booking, loyalty, or digital service experiences.
Experience with enterprise systems such as PMS, CRS, CRM, contact center, workforce management, or integration platforms.
Experience in 24/7, high-availability, revenue-impacting environments.

Responsibilities

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 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 for 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.
Manage Quality Engineering delivery across agile product teams and align testing strategy with product risk, system complexity, release cadence, and business criticality.
Partner with engineering, product, DevOps, infrastructure, data, and business teams so testing is planned early and executed consistently.
Ensure quality gates, release readiness criteria, and reporting are in place for agile and project-based delivery models.
Help design and operationalize AI Quality Engineering assistants that interpret stories, acceptance criteria, code, build artifacts, deployment schedules, and release plans.
Enable agents to generate, execute, monitor, and maintain automated test suites, and establish scheduled automated execution and reporting.
Implement self-healing and intelligent automation patterns to reduce maintenance effort and improve test reliability.
Ensure AI-generated tests, recommendations, and reports are explainable, auditable, and validated through appropriate engineering controls.
Own quality outcomes across functional quality, integration reliability, performance, load readiness, security validation, accessibility, and guest experience.
Embed automated testing into CI/CD pipelines and release workflows, and partner with engineering teams to improve testability, observability, code quality, and defect prevention.
Support release readiness with data-driven quality insights, risk assessments, and clear go/no-go recommendations.
Define and track quality metrics, including automation coverage, defect escape rate, test stability, execution duration, performance trends, load test results, and AI-assisted testing effectiveness.
Produce recurring automation summaries, release readiness dashboards, and quality risk insights; use data, AI-generated insights, and retrospectives to improve practices, tooling, coverage, and speed.
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 and load testing, test data management, and engineering-led quality practices.
Create a culture of ownership, curiosity, technical excellence, and continuous improvement, and align quality priorities with business outcomes.
Establish governance for automated and 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 on auditability, privacy, and regulatory expectations, and maintain controls for human review, exception handling, and approval of AI-assisted quality decisions.