About the Role:
At Camunda, we've moved our entire product organisation to an AI-native model — teams ship fast, autonomously, and close to the customer. That speed only creates value if we're building the right things, which makes validation more important than ever. As our Senior Product Analyst, you'll own the quantitative signal layer that keeps the whole pillar honest. You're joining at a rare moment: we're rebuilding our data platform into an AI-native one, and you won't be inheriting a finished system — you'll shape it. The goal is twofold and genuinely ambitious. First, give every product builder self-serve access to the signal they need to validate their own work, without waiting in a queue. Second, give our strategic PMs the infrastructure to measure what actually matters and build strategy on evidence, not intuition.
What You Will Do:
- 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.
What You Will Bring to the Role:
- 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.
Nice to Haves:
- 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.