Software Quality Assurance Manager

World Travel Holdings Inc•Remote,

6d•$135,000 - $150,000

About The Position

World Travel Holdings is seeking an experienced Quality Engineering & Test Automation Manager to lead a blended Quality Engineering team and build an AI-enabled, automation-first quality capability for World Travel Holdings (WTH). In this role, you will modernize testing tools and processes, design scalable automation and agentic quality workflows, mentor engineers across manual, exploratory, and automated testing disciplines, and partner closely with Product Management, Business Analysis, Engineering, Architecture, and DevOps teams to improve requirement quality, developer feedback cycles, release readiness, platform resiliency, and business outcomes across the software development lifecycle. World Travel Holdings, headquartered in Fort Lauderdale, FL, is the nation’s largest cruise agency and an award‑winning leisure travel company with nearly 40 travel brands. Through owned brands and private‑label partnerships, we sell cruises, villas, resort day passes, and luxury travel services. With a top-rated franchise, a UK cruise division, and a strong commitment to employee engagement and remote work, World Travel Holdings offers a people-focused culture. Learn more: WorldTravelHoldings.com

Requirements

Bachelor’s or master’s degree in computer science, engineering, related field, or equivalent practical experience.
7+ years of experience in Quality Engineering, software testing, or test automation, including 2+ years in a leadership or management role.
Deep hands-on experience designing, implementing, maintaining, and scaling test automation frameworks, including framework architecture, reusable components, coding standards, governance practices, and the practical application of AI-assisted Quality Engineering capabilities.
Proficiency in Java, Python, JavaScript, or a similar programming language, with the ability to guide automation of code quality, maintainability, and reusable framework design.
Experience integrating automated tests, quality gates, framework health checks, and reporting into CI/CD pipelines.
Demonstrated ability to modernize legacy Quality Engineering tools, consolidate fragmented testing approaches, and guide teams toward a future-state automation framework.
Demonstrated experience advancing shift-left quality practices through early collaboration on requirements, acceptance criteria, solution design, testability, and delivery risk.
Strong understanding of Agile/Scrum delivery, the software testing lifecycle, and how Quality Engineering practices support product delivery and release readiness.
Strong analytical, communication, and leadership skills, with the ability to influence technical direction, explain framework and quality tradeoffs, and align Product Management, Business Analysis, Engineering, Architecture, DevOps, and Quality Engineering stakeholders around modernization priorities, business risk, and quality outcomes.
Experience evaluating or implementing AI-assisted software engineering or Quality Engineering capabilities, including test generation, test maintenance, coverage analysis, release readiness, or agentic workflows preferred.

Nice To Haves

Experience with cloud platforms such as AWS, Azure, or GCP and containerization technologies such as Docker or Kubernetes a plus.
Experience with performance testing tools such as JMeter, Gatling, or Locust; security testing practices; test management platforms such as TestRail or Zephyr; API mocking; service-level API testing; SQL queries; and database testing across SQL and NoSQL databases preferred.
Travel industry experience ideal.

Responsibilities

Lead, mentor, and develop a blended Quality Engineering team spanning manual, exploratory, regression, API, performance, accessibility, and automated testing.
Manage Quality Engineering team capacity, priorities, and delivery across active initiatives while simplifying, standardizing, and modernizing team processes.
Establish an onboarding process that helps Quality Engineering team members ramp quickly and consistently.
Build strong working partnerships across Product Management, Business Analysis, Engineering, Architecture, DevOps, and Quality Engineering to establish shared ownership of quality throughout planning, design, development, delivery, and operation.
Own the design, implementation, and ongoing evolution of a modern, scalable automation framework supporting web, mobile, accessibility, API, integration, performance, and regression testing.
Define the automation framework architecture, technical standards, tool selection criteria, coding patterns, and governance practices needed to create a consistent enterprise Quality Engineering approach.
Assess existing Quality Engineering tools, test suites, environments, and processes to identify modernization, consolidation, and automation opportunities.
Build a roadmap for migrating current testing practices into the future-state automation framework, balancing near-term delivery needs with long-term scalability and maintainability.
Partner with Engineering and Architecture teams to ensure Quality Engineering strategies, automation frameworks, test environments, and quality controls support modernization initiatives, cloud readiness, platform resiliency, and long-term technical health.
Develop responsible adoption standards, measurement practices, and team skills for applying AI across Quality Engineering.
Design and evolve agentic Quality Engineering workflows that improve consistency, accelerate feedback, and reduce repetitive work while maintaining appropriate engineering oversight.
Lead adoption of AI-assisted test generation, execution, maintenance, optimization, coverage analysis, and quality insight capabilities.
Establish and communicate an AI-first Quality Engineering vision aligned with the broader Software Engineering transformation strategy.
Partner with Product Management and Business Analysts to improve requirement quality, acceptance criteria, maturity, testability, and shared understanding of business risk before development begins.
Advance shift-left quality practices by involving Quality Engineering earlier in discovery, refinement, solution design, and delivery planning.
Work across Product Management, Business Analysis, Engineering, Architecture, and DevOps to define risk-based quality strategies that connect business expectations, technical design, test coverage, operational readiness, and release decisions.
Partner with Engineering and DevOps teams to embed reliable automated test execution, quality gates, environment readiness checks, and actionable reporting into CI/CD pipelines.
Ensure appropriate test coverage across functional, regression, performance, load, accessibility, API, and integration testing.
Drive adoption of automation-first quality practices that reduce reliance on repetitive manual regression testing while preserving the value of exploratory, risk-based, and customer-focused testing.
Connect Quality Engineering priorities and reporting to customer experience, franchise experience, agent productivity, release confidence, and platform stability and reliability.
Partner with engineering leaders to identify quality improvements that increase development throughput and support faster, more confident release cycles.
Reduce developer feedback cycles by improving quality visibility earlier in the software development lifecycle and making results timely, reliable, and actionable.
Establish quality metrics and framework health reporting to track automation coverage, test effectiveness, execution reliability, defect trends, and release readiness.
Develop quality insights that help teams evaluate requirement readiness, acceptance criteria for maturity, automation effectiveness, release confidence, platform resiliency, and recurring indicators of technical health.
Evaluate emerging Quality Engineering and AI technologies to improve test creation, execution, maintenance, coverage analysis, developer experience, and quality reporting.
Manage resource allocation, planning, and delivery of automation framework initiatives, test modernization, and Quality Engineering improvements.
Advocate for modern, AI-first Quality Engineering practices across every stage of the software development lifecycle, and help engineering teams build the skills and operating practices needed to adopt them responsibly.
Use quality, productivity, and technical-health insights to identify improvement opportunities, communicate progress, and guide investment decisions across Product and Software Engineering.
Define transformation-oriented measures, including manual testing effort, automation adoption, AI testing adoption, engineering throughput, defect escape trends, release cycle speed, and deployment confidence.