Product Manager - Observability

• Observability Platform Product. Own the product vision and strategy for enterprise observability, including metrics, logs, traces, and the unified experiences built on top of them. Define how internal teams instrument services, consume telemetry, and gain insight into system behavior across a multi-cloud environment. Coordinate with Engineering teams to translate those priorities into products and features.
• AI-Powered Observability. Lead the introduction of AI into the observability lifecycle: anomaly detection, intelligent alert correlation, automated root cause analysis, natural-language querying of telemetry, and agentic incident workflows. Ensure the right human-in-the-loop moments are preserved where judgment matters.
• Internal Customer Partnership. Treat internal service teams as customers. Run discovery, gather feedback, track adoption and satisfaction, and translate real operational pain into prioritized product investments. Partner closely with SRE, incident management, and on-call communities.
• Cost & Efficiency. Own the economics of the platform. Balance telemetry volume, retention, and fidelity against cost, and partner with engineering on sampling, tiering, and vendor/build decisions.
• Execution and Communication. Drive delivery with a relentless focus on outcomes. Keep partners informed on plans, priorities, and progress, and serve as advocates for the platform, driving adoption through documentation, enablement, and evangelism across engineering teams.

• Platform product experience. 5+ years of product management experience, including 3+ years on infrastructure, observability, monitoring, or developer platform products consumed by large engineering organizations.
• Deep technical fluency in observability. You understand metrics, logs, traces, and the ecosystem. You can discuss SLIs/SLOs, alerting strategy, incident response, cardinality and cost tradeoffs, and distributed systems behavior. You’re comfortable reviewing design docs and debating tradeoffs with principal engineers. You don’t need to write production code, but you can read it and reason about it.
• Familiarity with the tooling landscape. Experience with commercial and open-source observability tooling (e.g., Datadog, Splunk, Grafana, Prometheus, New Relic, or similar) and a point of view on build vs. buy.
• AI-native approach. You understand how LLMs, agents, and ML techniques apply to operations (AIOps) and are excited to apply them thoughtfully to reduce toil and improve reliability.
• Internal platform product sense. You know how to drive adoption without a sales funnel: by earning trust, reducing friction, and delivering measurable value to internal customers.
• Strategic clarity under ambiguity. The landscape shifts quickly. You can synthesize signal from noise, make high-conviction bets with incomplete information, and adjust course without losing momentum.
• Strong influence and communication. You lead through influence across engineering, SRE, security, and program management, and communicate clearly with both technical teams and senior leaders.

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