About Netskope

Today, there's more data and users outside the enterprise than inside, causing the network perimeter as we know it to dissolve. We realized a new perimeter was needed, one that is built in the cloud and follows and protects data wherever it goes, so we started Netskope to redefine Cloud, Network and Data Security. 

Since 2012, we have built the market-leading cloud security company and an award-winning culture powered by hundreds of employees spread across offices in Santa Clara, St. Louis, Bangalore, London, Paris, Melbourne, Taipei, and Tokyo. Our core values are openness, honesty, and transparency, and we purposely developed our open desk layouts and large meeting spaces to support and promote partnerships, collaboration, and teamwork. From catered lunches and office celebrations to employee recognition events and social professional groups such as the Awesome Women of Netskope (AWON), we strive to keep work fun, supportive and interactive.  Visit us at Netskope Careers. Please follow us on LinkedIn and Twitter@Netskope.

About the Role

The Foundation Engineering team powers the core infrastructure of the Netskope NewEdge platform. We build and operate the services responsible for propagating runtime configuration state across 100+ data centers in near real-time, and we develop foundational cloud microservices that handle billions of events at scale. We are looking for a Senior Engineering Manager to lead this team — someone who combines strong people leadership with deep technical judgment, and who embraces an AI-first approach to engineering.

 

In this role, you will own the team's delivery outcomes end-to-end: from architecture and design through production operations. You will build a high-performing team, drive execution rigor, and ensure the foundational services your team owns are resilient, scalable, and built to the highest engineering standards.

 

What's in It for You

You will lead a high-impact team at the heart of the Netskope platform. The systems your team builds are critical infrastructure that every other service in the product depends on. You will shape how the team adopts AI-first engineering practices, drive full lifecycle ownership from requirements through production, and have direct influence on the technical direction of foundational cloud services used across the organization.

 

What You Will Be Doing

 

People Leadership and Execution

• Manage, coach, and mentor a team of engineers. Drive career growth through regular 1:1s, performance reviews, and individualized development plans.
• Build and grow the team through hiring — define roles, run interviews, raise the bar on talent.
• Run agile ceremonies (standups, sprint planning, retrospectives) and maintain delivery cadence.
• Plan and manage work using agile methodologies. Track milestones, manage timelines, and ensure the team delivers on commitments.
• Collaborate closely with Product Management, Program Management, and peer engineering teams to align on priorities and execution plans.
• Establish and maintain engineering best practices: code review culture, test-driven development, and continuous improvement.
• Deliver regular progress reports and communicate status, risks, and decisions transparently to leadership.

 

Technical Leadership

• Drive the architecture and design of next-generation foundational cloud services, including config propagation, runtime state synchronization, and platform infrastructure.
• Ensure the scalability, reliability, and availability of the services your team owns across a global footprint of 100+ data centers.
• Evaluate and adopt open-source technologies that best fit our needs.
• Coordinate cross-team technical dependencies with other service teams, SRE, and support to ensure end-to-end system health.
• Maintain hands-on engineering credibility — review designs, unblock technical decisions, and set the quality bar for the team's output.

 

AI-First Engineering

• Champion AI-assisted development across the team. Drive adoption of AI tools for code generation, code review, testing, and documentation.
• Set and track AI adoption metrics — measure how AI tools accelerate delivery velocity, reduce toil, and improve code quality.
• Foster a culture of experimentation where the team actively explores new AI  capabilities and integrates them into daily engineering work