Palo Alto Networks is dedicated to protecting the digital way of life through innovative technology and collaboration. They are seeking a Senior Manager of Quality Engineering to define and implement comprehensive QA strategies, ensure the quality of software releases, and drive AI transformation within the organization.
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
Requirements:
- 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