Manager Quality Engineering

A Quality Engineering Manager at Quest Diagnostics would be responsible for designing, implementing, and maintaining automated testing frameworks to ensure the quality of software and systems. This role involves strategic planning, hands-on technical leadership, and collaboration with various teams to integrate testing into the software development lifecycle.

Duties & Responsibilities:

• Responsible for developing test automation strategies, goals and plans.
• Designing and Implementing Test Automation Frameworks includes selecting appropriate tools, defining test strategies, and developing reusable automation components.
• Provide technical guidance to Team of Test Automation Engineers , FTE and Vendors. Foster a culture of engineering excellence.
• Ensuring that testing is seamlessly integrated into the development process including CI/CD pipelines .
• Working with development, product, and other teams to align testing strategies and ensure quality objectives are met.
• Continuously researching and evaluating new automation tools and practices to improve testing efficiency. Bring GenAI and Agentic AI in test automation to increase productivity and reduce testing timelines.
• Ensuring that all testing practices align with relevant regulatory standards and guidelines.
• Identifying and resolving complex testing issues and contributing to the overall quality of the product.
• Creating and executing test plans for various types of testing, including sprint, regression, smoke and E2E .

Required Work Experience

Minimum 15 years of experience in architecting and designing automation frameworks.

Proficiency in languages like Python, Java, or JavaScript/TypeScript, and experience with test automation frameworks.

Preferred Work Experience

• Experience with various test automation tools and platforms, such as Webdriver.IO , UFT, Appium , Postman etc.
• Understanding cloud technologies like AWS, Azure, or GCP, and experience with cloud-based testing.
• Experience working within Agile development frameworks like Scrum or Kanban .
• Ability to effectively communicate technical information to both technical and non-technical audiences.
• Ability to troubleshoot complex testing issues and identify root causes.
• Experience in coaching and mentoring a team of test automation engineers for their growth.
• Experience integrating test automation frameworks/tools into CI/CD pipelines.
• Experience working in a regulated industry like healthcare , where compliance and quality are paramount, is a plus.
• Having experience in building and maintaining data test automation frameworks is plus.

Physical and Mental Requirements

• Sustained focus for architecture design, code review, and root cause analysis
• Rapid context switching across parallel initiatives and teams
• Systems thinking: modeling frameworks, pipelines, dependencies, risks
• Analytical problem solving under time and priority pressure
• Strategic planning and roadmap development
• Decision making with incomplete or evolving information
• Leadership: mentoring, conflict resolution, feedback delivery
• Clear written and verbal communication to technical and non‑technical audiences
• Judicious handling of sensitive, regulated, or proprietary data
• Continuous learning/adaptation to emerging tools (AI, cloud, frameworks)

Knowledge

• Software testing theory: test levels, types, risk-based coverage, QA metrics
• Test automation architectures: layered, keyword/data/behavior-driven, microservice and event-driven testing approaches
• Programming language ecosystems like Java (Maven/Gradle), Python (pytest), TypeScript/JavaScript (Node, npm)
• UI automation frameworks like WebdriverIO, Selenium, Appium
• API/Service testing: REST, SOAP, GraphQL, contract testing (PACT), schema validation
• CI/CD systems and deployment models: GitHub Actions, Azure DevOps, Jenkins, pipelines, quality gates
• Cloud & container platforms: AWS/Azure/GCP basics, Docker, Kubernetes, ephemeral test environments
• Test data management: masking, synthetic generation, data virtualization, database querying (SQL, Snowflake)
• Performance & reliability fundamentals: load, stress, scalability, observability (logs, metrics, traces)
• Security, accessibility, and compliance considerations (HIPAA, audit trails, traceability)
• Version control workflows: branching strategies, code review standards
• AI/GenAI in testing: self-healing locators, intelligent test selection, generative test design, agent orchestration
• Environment management: service virtualization, mocking, stubs, test isolation strategies
• Quality metrics & analytics: coverage, flakiness, defect leakage, MTTR, ROI calculations