Product Manager, BigQuery (Europe)

The Role

At Rabbit, the unit of value is shipped product, not lines of code — and not tickets. AI handles the typing and our engineers handle the building. The scarce skill is deciding which problems are worth solving, proving it with evidence, and being accountable for whether what we ship works for our customers and for our business. We’re hiring a Product Manager to own that.

You’ll be the acknowledged expert on our customers — data platform teams running some of the largest BigQuery workloads in Europe and North America — on how they actually use Rabbit, on the economics of our product, and on where BigQuery and GCP are heading. Each quarter you’ll turn our product strategy into the two or three problems that matter most, hand our teams problems to solve rather than features to build, and own the value and viability of what ships.

What makes this different from a PM job at a larger company: there are no layers between you and the customer, the founders, or the engineers. Our product engineers, forward deployed engineers and TAMs already carry deep customer context and prototype constantly. Your job is not to route their tickets. It’s to make sure we’re solving the right problems, to bring the evidence, and to be accountable for the outcomes — dollars saved, features adopted, accounts expanded and renewed.

You’ll be our first dedicated product manager, working directly with the founders. They own the vision. You own turning it into a strategy the teams can act on — and the results.

Initial domain: BigQuery cost optimization — slot and reservation management, pricing models, storage, query-level optimization. As Rabbit’s scope expands across GCP and toward running the data platform autonomously, so will yours.

What You’ll Actually Do

• Own outcomes, not output — you’re accountable for the value and business viability of what we ship, measured in customer dollars saved, features adopted, accounts expanded and renewed
• Turn strategy into quarterly team objectives — pick the two or three problems that matter most, say no to the rest, and give teams problems to solve, not features to build
• Run the insight engine — live in our product and billing data, talk to customers every week, track Google’s pricing and platform changes and what competitors ship, and connect the dots before anyone else does
• Discover before we build — de-risk value and viability with prototypes, customer pilots and pricing tests; kill ideas early with evidence and take credit for what we didn’t build
• Prototype with AI when it’s the fastest way to learn — a working prototype in front of a customer beats a slide; use Claude Code, Cursor or whatever gets you there
• Own viability — pricing and packaging, enterprise procurement and security review, GTM enablement for our TAMs and Customer Engineers, release readiness and the story sales tells
• Know the customer better than anyone — know our different clients and their characteristics: how their teams, workloads and priorities differ, and what each of them needs from Rabbit

Must-have:

• 5+ years of product management experience on B2B software with real customers — you’ve owned a product or product area end-to-end and can show the business outcomes it produced
• Enthusiasm that shows — you’re genuinely excited about data platforms, cloud economics and AI agents; you’d rather demo a prototype than present a slide, and you get uneasy after a week without a customer conversation
• Customer discovery as a habit — you run interviews and pilots with enterprise teams yourself, without an Account Executive holding your hand
• Data fluency — comfortable in SQL against usage and billing data; you decide from evidence and know a vanity metric when you see one
• Business fluency — you understand how a B2B SaaS company makes money: pricing, packaging, enterprise procurement, security review, renewals and expansion
• Fluent with AI and agentic tools (Claude Code, Cursor, Copilot, LLMs) for research synthesis, data analysis and prototyping — on real work, not demos
• Exceptional written and verbal English — you’ll write the narrative a data engineer, a VP and a procurement team all understand
• Comfort with ambiguity and autonomy — small team, no middle management; you own outcomes and chase things yourself

Strong plus:

• Technical fluency — you can hold a design conversation with senior data engineers, read a query plan, and explain how a cloud data warehouse bills
• Technical background — software or data engineering, solutions architecture, or a technical degree
• Technical product experience — developer tools, data infrastructure, cloud platforms, or anything sold to engineers
• Google Cloud knowledge — BigQuery (slots, reservations, editions pricing, INFORMATION_SCHEMA) and GCP billing. Equivalent depth on another cloud data warehouse plus the appetite to ramp on BigQuery fast also works — we’ll help, and we’ll expect you to be the acknowledged expert within 90 days
• Shipped AI-powered or autonomous features — and built the trust and guardrails that let enterprises adopt them
• Founder or first-product-hire experience — you’ve written the motion rather than followed one

Why This Role

• Your decisions ship. No roadmap theater. The problem you pick this quarter is saving a Fortune 500 customer millions the next.
• Direct customer exposure. Data platform teams at some of the largest GCP workloads in Europe and North America. You see impact in dollars, on real workloads.
• Hard problems. Autonomous optimization of petabyte-scale BigQuery, earning the trust to let software change production, pricing an autonomous platform. Not CRUD, not another dashboard.
• Real product ownership. You own the problems and the outcomes. Founders own the vision with you. Engineers own the how.
• AI-first leverage. You’ll work with the best agentic tools and a team that expects you to use them — your reach compounds.

How We Work — AI-First, Agentic by Default

Rabbit is an AI-first company, in product as much as in engineering. You won’t write a PRD and hand it over. You’ll dig into billing data with agents, synthesize discovery with LLMs, and when a prototype is the fastest way to learn, you can build it yourself. We measure product management in outcomes shipped, not documents produced.

Product creators, not coordinators. If your definition of the job is backlog grooming, sprint ceremonies and status decks, this won’t fit. If you combine strong judgment with agentic tools to compress discovery from months to days, you’ll thrive.

How to Apply

Send your CV or LinkedIn, plus a short note on a product decision you made from evidence that changed a business outcome — what was the customer problem, what evidence did you gather, what did you decide to build or kill, and what changed for the customer and the business when it shipped. Bonus points if you describe how you used AI agents to compress the cycle.