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The job details are as follows:OCLC's Quality Assurance organization supports quality across the delivery lifecycle through defect prevention, risk management, effective testing, and continuous improvement.
The Manager of Test Engineering leads Quality Test Engineers across OCLC products and services, overseeing QA strategy, staffing, testing, automation, metrics, AI adoption, and release readiness while partnering with Product and Engineering to improve quality, productivity, and delivery confidence.
This role advances QA modernization through AI-assisted testing, intelligent automation, performance engineering, quality analytics, and tool/framework adoption to improve testing effectiveness, feedback speed, productivity, and release confidence.Responsibilities

• Lead, coach, and develop Quality Engineers across multiple products and initiatives.
• Manage QA planning, staffing, prioritization, capacity, and execution to support delivery commitments.
• Define and execute balanced testing strategies for exploratory, manual, accessibility, functional, integration, regression, acceptance, performance, and release testing, using shift-left and risk-based practices to accelerate feedback and prevent defects.
• Lead QA modernization initiatives through automation, AI-enabled agentic testing, quality analytics, tool and framework adoption, and modern Quality Engineering practices that improve testing effectiveness, productivity, and release confidence.
• Define, monitor, and communicate quality and delivery metrics (e.g., defect escape rates, test coverage, change failure rates, release readiness, RCA findings), and use insights to drive continuous improvement and measurable outcomes.
• Establish quality standards, governance practices, and scalable quality ownership models across teams (including coaching Engineering teams on effective automated testing practices and adoption of standard frameworks).
• Define and maintain CI/CD quality gates and release criteria, improve pipeline signal quality (flake reduction, suite health, triage/quarantine practices), and establish clear escalation paths when quality risk is high.
• Partner with Product, Engineering, Operations/SRE, and business stakeholders to embed quality throughout the SDLC, ensure transparency of quality risks and testing progress, and improve reliability, scalability, performance, and customer experience.
• Influence architecture and design for testability by participating in design/architecture reviews and promoting practices such as contract testing, backward compatibility validation, and testability requirements.
• Own strategy for test environments and test data management (including synthetic test data generation and privacy-safe practices) and integrate production/customer signals and post-incident learnings into prevention and regression strategies.
• Assess release readiness, communicate quality risk, and provide recommendations that support informed delivery decisions, including alignment with security and privacy requirements where applicable.

Qualifications

• Bachelor's degree in information technology, Computer Science, Library Science, or a related discipline; or equivalent experience.
• Understanding of AI-assisted testing, intelligent automation, and modern Quality Engineering practices.
• Experience leading, coaching, and developing high-performing QA teams supporting complex software products and delivery organizations.
• Strong knowledge of software testing methodologies, including manual, exploratory, UI, API, database validation, accessibility, functional, integration, regression, acceptance, and performance testing.
• Experience managing QA planning, execution, quality reporting, metrics, and release readiness activities.
• Experience defining quality objectives, managing quality risk, and using metrics to drive data-informed decision-making.
• Experience with automation frameworks and technologies such as Playwright, Selenium, Cucumber, Python, Java, or equivalent solutions.
• Knowledge of performance engineering principles, including scalability, reliability, capacity planning, and load testing using tools such as K6, JMeter, BlazeMeter, WebLOAD, or similar solutions.
• Experience with Agile methodologies, GitHub-based development workflows, CI/CD pipelines, Jenkins, Jira, quality reporting platforms, and modern AI-enabled engineering tools.

Technical Focus

• Test strategy, planning, execution, and release readiness
• AI-powered quality analytics and engineering productivity improvements
• Test data management and synthetic test data generation
• Responsible AI adoption, governance, security, and privacy
• Manual, exploratory, UI, API, database validation, accessibility, functional, integration, regression, acceptance, and performance testing
• Automation frameworks and CI/CD quality integration
• Quality metrics, analytics, and executive reporting
• Root Cause Analysis (RCA), defect prevention, and quality governance
• Performance engineering, scalability, reliability, capacity planning, and load testing
• Quality Engineering transformation, modernization, and shift-left testing
• AI-assisted test design, test generation, and intelligent regression testing

Non-Technical Skills

• Passion for coaching and mentoring
• Driven by measurable results
• Positive attitude
• Tenacious problem solver
• Possess an innovation mindset
• Naturally inquisitive and curious

Working Conditions:Normal office environment.

ADA/EAA:The above statements cover what are generally believed to be principal and essential functions of this job. Specific circumstances may allow or require some people assigned to the job to perform a somewhat different combination of duties.