Software Quality Assurance Manager

United StatesRemoteFull-timemid

OtherQuality Assurance Manager

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Quality Assurance Manager based in the United States.

Technical Tools

OtherQuality Assurance Manager

This is a leadership role focused on building a modern, automation-first Quality Engineering capability across the software development lifecycle.
You will lead a blended team covering manual, exploratory, API, performance, accessibility, regression, and automated testing.
The role combines people leadership with hands-on ownership of scalable automation frameworks, AI-enabled testing, and quality transformation.
You will work closely with Product Management, Business Analysis, Engineering, Architecture, and DevOps to embed quality earlier into delivery.
A major focus will be advancing shift-left practices, strengthening CI/CD quality gates, and improving release confidence, platform resiliency, and developer feedback cycles.
You will also establish quality metrics and responsible AI practices that demonstrate the impact of testing on productivity, customer experience, and business outcomes.
This fully remote opportunity is ideal for an experienced Quality Engineering leader who enjoys modernization, innovation, mentoring, and building scalable practices.

Lead, mentor, and develop a Quality Engineering team across manual, exploratory, regression, API, performance, accessibility, and automated testing.
Manage team capacity, priorities, delivery, onboarding, and process improvement while establishing shared ownership of quality across technology teams.
Design, implement, and continuously evolve a scalable automation framework supporting web, mobile, API, integration, performance, accessibility, and regression testing.
Define automation architecture, technical standards, coding patterns, tool-selection criteria, governance practices, and enterprise Quality Engineering standards.
Assess existing testing tools, suites, environments, and processes to identify modernization, consolidation, and automation opportunities.
Develop and execute a roadmap for migrating legacy testing practices toward a scalable future-state automation framework.
Partner with Engineering and Architecture to align testing strategies, environments, quality controls, cloud readiness, platform resiliency, and long-term technical health.
Develop responsible standards and measurement practices for applying AI across Quality Engineering.
Design and evolve agentic Quality Engineering workflows that improve consistency, accelerate feedback, reduce repetitive work, and maintain appropriate engineering oversight.
Lead adoption of AI-assisted test generation, execution, maintenance, optimization, coverage analysis, and quality insights.
Establish an AI-first Quality Engineering vision aligned with broader software engineering transformation initiatives.
Partner with Product Management and Business Analysts to improve requirements, acceptance criteria, testability, and understanding of business risk before development begins.
Advance shift-left quality practices across discovery, refinement, solution design, and delivery planning.
Define risk-based quality strategies connecting business expectations, technical design, test coverage, operational readiness, and release decisions.
Embed automated testing, quality gates, environment checks, and actionable reporting into CI/CD pipelines.
Ensure appropriate functional, regression, performance, load, accessibility, API, and integration coverage.
Reduce dependence on repetitive manual regression testing while preserving exploratory, risk-based, and customer-focused testing.
Connect Quality Engineering priorities and reporting to customer experience, employee productivity, release confidence, platform stability, and development throughput.
Establish quality metrics covering automation coverage, test effectiveness, execution reliability, defect trends, release readiness, framework health, and technical health.
Evaluate emerging Quality Engineering and AI technologies to improve test creation, execution, maintenance, coverage, developer experience, and reporting.
Manage resources and delivery for automation framework initiatives, test modernization, and broader Quality Engineering improvements.
Define transformation measures such as manual testing effort, automation adoption, AI testing adoption, engineering throughput, defect escape trends, release-cycle speed, and deployment confidence.

Requirements

~2 min read

Bachelor’s or master’s degree in Computer Science, Engineering, a related field, or equivalent practical experience.
7+ years of experience in Quality Engineering, software testing, or test automation, including at least 2 years in a leadership or management role.
Deep hands-on experience designing, implementing, maintaining, and scaling automated testing frameworks, including architecture, reusable components, coding standards, and governance.
Proficiency in Java, Python, JavaScript, or a similar programming language, with the ability to guide automation design, code quality, maintainability, and reusable framework development.
Experience integrating automated testing, quality gates, framework health checks, and reporting into CI/CD pipelines.
Demonstrated success modernizing legacy Quality Engineering tools, consolidating fragmented testing approaches, and establishing future-state automation frameworks.
Experience advancing shift-left quality through early involvement in requirements, acceptance criteria, solution design, testability, and delivery risk.
Strong understanding of Agile/Scrum methodologies, the software testing lifecycle, and Quality Engineering practices supporting product delivery and release readiness.
Strong analytical, communication, leadership, and influencing skills, with the ability to align Product Management, Business Analysis, Engineering, Architecture, DevOps, and Quality Engineering stakeholders.
Experience evaluating or implementing AI-assisted software engineering or Quality Engineering capabilities, including test generation, maintenance, coverage analysis, release readiness, or agentic workflows, is preferred.
Experience with AWS, Azure, GCP, Docker, or Kubernetes is a plus.
Familiarity with performance testing tools such as JMeter, Gatling, or Locust is preferred.
Experience with security testing, TestRail or Zephyr, API mocking, service-level API testing, SQL, and SQL/NoSQL database testing is advantageous.
Travel industry experience is a plus.
Strong ability to communicate technical tradeoffs, influence modernization priorities, and connect quality initiatives to measurable business outcomes.