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 software development lifecycle through automation-first practices, AI-assisted testing, continuous testing, and measurable engineering standards that improve delivery speed, reliability, and guest-facing quality. The manager drives adoption of AI-assisted Quality Engineering, including agentic agents that can interpret user stories, acceptance criteria, code changes, build outputs, deployments, and release plans to generate, execute, and maintain automated tests. Testing spans smoke, functional, API, regression, user experience, integration, performance, load, release validation, and other needs. The role manages quality engineers and automation resources across agile product teams and structured project delivery efforts. It establishes consistent standards, reporting, and continuous improvement to ensure Wynn’s digital platforms and enterprise systems meet high standards for quality, performance, security, reliability, and guest experience.

Requirements

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

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 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 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 and testing efforts across agile product teams.
Align testing strategy to product risk, system complexity, release cadence, and business criticality.
Partner with engineering, product, DevOps, infrastructure, data, and business teams to plan and execute testing consistently.
Ensure quality gates, release readiness criteria, and reporting are in place for agile and project-based delivery.
Help design and operationalize AI Quality Engineering assistants that interpret user stories, acceptance criteria, code, build artifacts, deployment schedules, and release plans.
Enable agentic agents to generate, execute, monitor, and maintain automated test suites across relevant testing types.
Establish scheduled automated test execution and reporting that summarizes results, defect trends, coverage gaps, performance concerns, and release risks.
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 engineering controls.
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 with data-driven quality insights, risk assessments, and clear go/no-go recommendations.
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 recurring automation summaries, release readiness dashboards, and quality risk insights.
Use data, AI-generated insights, and team retrospectives to improve testing 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 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.
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 support auditability, privacy, and regulatory expectations.
Maintain controls for human review, exception handling, and approval of AI-assisted quality decisions.