Sr. Manager, Foundation Services

Netskope•Santa Clara, CA

18h

About The Position

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.

Requirements

A minimum of 15+ years of software development experience with 5+ years in an engineering management role leading teams of 8+ engineers.
A proven track record of building high-performance engineering teams and defining effective processes that accelerate delivery with high quality.
Demonstrable technical accomplishments and earned engineering credibility in distributed systems, microservices, and cloud infrastructure.
Programming proficiency in Go (primary) and Python.
Experience with agile/scrum methodologies and sprint management.
Strong cross-functional collaboration skills — ability to work effectively with Product Management, Program Management, SRE, and peer engineering teams.
Excellent verbal and written communication skills, with the ability to communicate transparently and consistently with both technical and non-technical stakeholders.
A strong customer focus and willingness to roll up your sleeves to respond to production needs promptly.
BS in Computer Science or equivalent technical degree required.

Nice To Haves

Familiarity with C/C++ is a plus.
Experience building or operating config management, data synchronization, or infrastructure services at scale is a plus.
Hands-on experience with AI-assisted development tools and a track record of driving AI adoption within engineering teams.
Experience in cybersecurity or network security technologies.
Experience with Kubernetes and containerized deployments at scale.
Familiarity with observability practices, monitoring, and on-call operations.
Track record of reducing operational toil through automation and AI-driven tooling.
Experience working with remote teams across multiple countries and time zones.
MS in Computer Science is strongly preferred.

Responsibilities

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.
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.
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 workflows.
Use AI-first practices to reduce operational burden, improve on-call efficiency, and raise the team's overall engineering leverage.