The opportunity
At Datadog, we're on a mission to build the best platform in the world for engineers to understand and scale their systems, applications, and teams. We operate at high scale, enabling seamless collaboration and problem-solving among Dev, Ops, and Security teams globally for tens of thousands of companies.
What you'll do
Own the product strategy and long-term vision for a core area of APM,: grounded in deep understanding of customers, their performance challenges, telemetry workflows, and the competitive landscape
Lead strategic conversations with design partners and key customers to: uncover complex performance issues, validate product bets, and guide solutions from early prototypes through General Availability
Define and drive delivery of the next generation of APM features with: engineering and design leadership, especially agentic onboarding and the out-of-the-box experience, AI-driven troubleshooting, and root-cause analysis
Make complex performance data, traces, and systems behavior understandable: and actionable, even for engineers who are new to observability
Set direction across engineering, design, and partner PM teams by framing: ambiguous problems clearly, driving alignment on trade-offs, and delivering seamless cross-product experiences and unified investigative workflows
Partner with marketing, sales, customer success, support, and other: cross-functional groups to define and lead Go-To-Market strategy for your product area
What they're looking for
- Mentor and provide guidance to other product managers, raising the bar for: product craft and customer-centric thinking across the team
- Own adoption, retention, and business impact metrics for your product area,: and communicate progress and strategy to senior leadership and executives
- You have +5 years of experience as a Product Manager, ideally working on a: developer-focused SaaS or infrastructure product, with a track record of owning a product area end to end
- You have experience leading production-level AI projects, from conception: through launch and iteration, ideally involving ML-driven insights, automated workflows, or intelligent guidance in complex systems
