Job Description
Join us at Zendesk, where we're on a mission to power exceptional service for every person on the planet. We're accelerating that ambition by building products rooted in AI, automation, and intelligent customer experiences, because behind every interaction lies an opportunity to make a human connection.
We’re seeking a Senior Engineering Manager (M7) to lead and grow a high-performing engineering organization that builds scalable, reliable SaaS systems used by millions of customers. In this role you’ll partner with Reliability, Product, Engineering Productivity, Design, Data, and Security to deliver AI-enabled tools/features, improve platform resilience, and accelerate developer velocity — all while developing engineering leaders and shaping technical strategy for long-term impact.
What you’ll be doing
Lead, coach, and grow engineering teams or managers to deliver high-impact, engineering productivity, customer-facing features and platform reliability improvements.
Contribute technically; lead by example to tackle complex engineering challenges through individual contributions and AI-assisted development.
Maintain relationships with the wider technical community; ensure Zendesk remains at the vanguard of AI innovation and adoption of the latest technologies.
Set technical strategy and execution plans with Reliability and Productivity, Product and Design; prioritize work to maximize velocity/customer value, operational stability, platform health, and engineering efficiency.
Own delivery and quality: define KPIs (latency, uptime, MTTR, deployment frequency), track outcomes, and remove blockers to meet SLAs and business goals.
Drive architecture and engineering tradeoffs for scalability, security, cost, and observability; partner with architects and senior engineers to evolve platform design.
Recruit and retain top engineering talent; establish hiring plans, interview processes, and onboarding that scale.
Foster a healthy engineering culture: career development, performance calibration, diversity of thought, and psychological safety.
Partner cross-functionally to launch AI-enabled tools/features responsibly — ensuring data quality, model lifecycle considerations, cost/benefit calculations, and clear operational ownership.
Evangelize best practices (CI/CD, automated testing, monitoring, incident response) and continuous improvement across teams.
Maintain a culture of versatility; keep the team nimble in order to address platform needs in the current environment that change quickly.