Quality Engineering Manager
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Requirements
• Bachelor’s degree in a related field and a minimum of 8 years of relevant experience in cloud/infrastructure technologies, IT consulting/support, systems administration, network operations, software development/support, or technology solutions. • 4–6 years of experience in customer-facing roles and leading projects. • Strong experience with test automation tools and performance testing. • Familiarity with regulatory frameworks such as GxP, FDA 21 CFR Part 11, or GDPR. • Advanced problem-solving and analytical skills, with the ability to identify root causes and recommend scalable solutions. • Strong written and verbal communication skills, capable of conveying complex technical concepts to diverse audiences. • Proficient in project management processes and tools, including scheduling, budgeting, and status reporting across multiple workstreams. • Skilled in writing complex reports and documentation, presenting findings in a clear, concise, and actionable format. • Demonstrated leadership and project management capabilities, with experience leading cross-functional initiatives. • Recognized as a technical resource, providing guidance and expertise to team members and influencing QA practices. • Proven ability to lead and manage complex projects or functional areas, ensuring alignment with strategic objectives. • Contributes to the development and execution of departmental strategies, bringing a quality-focused perspective to planning discussions. • Advanced Quality Engineering Expertise: Deep knowledge of functional, regression, performance, and AI/ML testing methodologies, with the ability to design and oversee testing strategies across multiple product lines. • Advanced Quality Engineering Expertise: • Test Automation & CI/CD Proficiency: Strong capability in building and maintaining scalable automated test frameworks and optimizing CI/CD pipeline integrations to support efficient, reliable releases. • Test Automation & CI/CD Proficiency: • AI/ML Validation & Quality Controls: Competency in validating AI/ML models, including bias detection, reproducibility, traceability, and performance benchmarking, applying appropriate quality standards. • AI/ML Validation & Quality Controls: • Regulatory & Compliance Knowledge : Working knowledge of GxP, FDA 21 CFR Part 11, ISO, GDPR, and other applicable quality and data protection frameworks, with the ability to embed compliance throughout the PDLC/SDLC. • Regulatory & Compliance Knowledge : • Cross‑Functional Collaboration & Communication: Ability to communicate complex technical concepts clearly and translate test results into actionable insights for engineering, product, operations, and senior stakeholders. • Cross‑Functional Collaboration & Communication: • Technical Leadership & Mentoring :Demonstrated ability to guide and mentor QA engineers, shape QA methodologies, and provide thought leadership that promotes consistency and quality across teams. • Technical Leadership & Mentoring : • Analytical & Problem‑Solving Strength: Advanced analytical aptitude for identifying root causes, understanding systemic defects or risks, and developing scalable, long‑term solutions. • Analytical & Problem‑Solving Strength: • Quality Metrics, Audit Readiness & Governance : Skilled at interpreting quality metrics, assessing release readiness, maintaining audit‑ready documentation, and supporting internal and external inspection activities. • Quality Metrics, Audit Readiness & Governance : • Project & Release Management Competency: Strong capability in managing multiple workstreams and deliverables, ensuring alignment with timelines, scope, and quality objectives across cross‑functional initiatives. • Project & Release Management Competency: • Continuous Improvement & Innovation Mindset: Commitment to evaluating emerging technologies, challenging existing practices, and implementing improvements that enhance quality, efficiency, and team capability. • Continuous Improvement & Innovation Mindset: • Any data provided as a part of this application will be stored in accordance with our Privacy Policy. For CA applicants, please also refer to our CA Privacy Notice.
Responsibilities
• Lead the design and execution of functional, regression, performance, and AI/ML testing strategies across multiple product lines or modules. • Develop and maintain scalable automated test frameworks; optimize CI/CD pipeline integrations for efficiency and reliability. • Collaborate with engineering, product, and operations teams to embed quality throughout the PDLC and SDLC. • Ensure rigorous validation of AI/ML models, including bias detection, reproducibility, and performance benchmarking. • Maintain compliance with internal quality systems and external regulatory standards (e.g., FDA, ISO); contribute to policy and SOP development. • Support internal audit readiness and lead preparation efforts for external inspections. • Monitor and analyze quality metrics, release readiness, and risk assessments to inform decision-making. • Drive continuous improvement initiatives and champion QA best practices across teams. • Lead cross-functional QA initiatives of moderate to high complexity, including planning, execution, and stakeholder alignment. • Provide technical leadership and mentorship, shaping QA methodologies and coaching junior engineers on advanced testing techniques. • Drive innovation and continuous learning by evaluating emerging technologies and integrating them into QA strategies. • Establish and enforce QA standards and governance to ensure consistency, traceability, and auditability across projects. • Facilitate strategic communication, translating complex technical findings into actionable insights for senior stakeholders. • Contribute to vendor strategy and compliance oversight, including qualification, performance evaluation, and risk mitigation. • Play a key role in strategic planning and decision-making, providing quality-focused input that informs platform architecture, release strategy, and operational priorities. • Other duties as assigned, including cross-functional collaboration and support of organizational quality goals.
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