Senior Product Operations Manager

Camunda is an enterprise platform focused on agentic orchestration, enabling organizations to coordinate AI agents, people, and systems. The Senior Product Operations Manager will build and maintain measurement infrastructure for outcome metrics and design a self-serve data layer for product builders, ensuring that metrics are credible and actionable.

Responsibilities:

• Build and maintain the measurement infrastructure for our outcome metrics — time to first production, enterprise adoption, production confidence in regulated industries — so the metrics that matter are live, current, and credible
• Design and build the AI-native, self-serve data layer that lets product builders answer their own questions and validate their own work, instead of raising a ticket and waiting on analyst bandwidth
• Set up measurement before teams ship, not after — so "did this land?" has an answer while teams can still act on it
• Compile the quantitative signal that strategic PMs use to set direction, pairing your numbers with the qualitative "why" our researchers bring, so decisions rest on a complete evidence base
• Own the data pipelines end to end — this is a continuous measurement system you build and run, not a series of one-off analyses
• Surface what the data actually says, even when it contradicts a direction someone has already committed to — the credibility of the whole system depends on it

Requirements:

• 5+ years in product, data, or growth analytics in a B2B SaaS or enterprise software environment
• Experience building measurement infrastructure from low maturity — making the foundational architecture decisions, not just maintaining an established system
• A track record of defining outcome metrics with product teams: turning fuzzy goals into measurable north stars and tracking them over time
• Strong SQL and data engineering skills — you can own the pipelines yourself, not just consume dashboards
• The judgment and directness to present findings that challenge a senior stakeholder's position, and stand behind the data
• Experience building self-serve analytics tooling that non-analysts — PMs, designers, engineers — actually adopted and used
• Familiarity with AI-assisted data workflows: LLM-powered querying, AI-generated insight, or analytics embedded in AI-native build pipelines
• Background supporting both a central team and multiple autonomous product teams at once
• Exposure to process automation, orchestration, or developer tooling, where simple event funnels don't map cleanly to value
• Experience pairing closely with qualitative researchers on mixed-methods evidence