Note: The job is a remote job and is open to candidates in USA. Palo Alto Networks is dedicated to protecting the digital way of life through innovative technology and collaboration. The Senior Manager of Quality Engineering will lead the QA strategy and ensure the quality of software releases, while also driving AI transformation and fostering a high-performance culture within the team.
Responsibilities
- Define, implement, and evolve the comprehensive QA strategy, test frameworks, and quality metrics across all product lines
- Take ultimate accountability for the quality of releases, ensuring software is scalable, secure, and high-performing
- Partner closely with Product Management, Engineering, and DevOps to embed quality standards early in the SDLC (Shift-Left approach)
- Identify, assess, and mitigate product quality risks before they impact production
- Research, evaluate, and implement cutting-edge AI/ML-driven testing tools and generative AI utilities to optimize test case generation, execution, and maintenance
- Act as a mentor and change agent, designing training pathways to help traditional manual and automation QA engineers transition into AI-augmented testing roles
- Evolutionize existing automation frameworks by incorporating predictive analytics, self-healing test scripts, and intelligent bug-clustering
- Establish baseline metrics to measure the ROI, speed, and accuracy improvements gained through AI adoption
- Foster a high-performance culture rooted in continuous learning, innovation, and psychological safety
- Manage headcount, budget, and resource allocation across multiple agile squads
- Set clear goals, conduct regular performance reviews, and guide career development for QA leads and engineers
Skills
- 10+ years of experience in Software Quality Assurance, with at least 5+ years in a dedicated people management or leadership role
- Proven track record of owning product quality end-to-end for scalable SaaS, cloud, or enterprise applications
- Demonstrated experience leading a team through a significant technological pivot (e.g., manual-to-automation or automation-to-AI transition)
- Deep expertise in modern automation frameworks (e.g., Playwright, Selenium, Pytest) and CI/CD pipelines (Jenkins, GitLab)
- Strong conceptual and practical understanding of how Generative AI, LLMs, and ML tools can be applied to the testing lifecycle
- Solid grasp of coding/scripting languages (Python, JavaScript, or Java)
- Exceptional ability to guide teams through organizational and technological shifts with empathy and clarity
- Ability to balance long-term AI innovation with the daily demands of product shipping
- Crisp and compelling communication skills, capable of explaining complex AI concepts to stakeholders and engineering teams alike
Benefits
- Equity package
- Comprehensive benefits
- Dedicated budget for AI tools, training, and professional development
- Flexible/hybrid work environment
Company Overview
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