The Role

At Rabbit, the unit of value is shipped product, not lines of code. AI handles the typing. The scarce skill is deciding what to build, validating it with real customers, and getting it to GA. We’re hiring a Product Engineer who lives that loop on BigQuery — Rabbit’s primary optimization surface and the one our customers spend the most on.

Two responsibilities, one feedback loop:

1. Ship features end-to-end — bring the idea (or pick one off the shelf), prototype it, pilot with a handful of customers, iterate on real feedback, take it to GA.
2. Customer success on what’s already shipped — help enterprise customers adopt existing Rabbit features, surface what’s missing, and unblock them when something breaks.

Initial domain: BigQuery cost optimization — slot management, reservation models, job-level optimization, query analysis at petabyte scale. As Rabbit’s optimization scope expands across GCP, so will yours.

What You’ll Actually Do

• Identify optimization opportunities by working directly with enterprise customers and reading their workloads
• Prototype features fast — proof-of-concept first, polish later
• Run customer pilots: design the experiment, gather feedback, decide whether to iterate or kill
• Take validated prototypes to GA: production-grade pipelines, APIs, infrastructure-as-code
• Drive adoption of shipped features — onboarding, technical Q&A, troubleshooting
• Investigate and resolve real-world data anomalies, pipeline issues, and BigQuery cost questions
• Feed field signal into product direction — you’ll have more customer context than anyone

How We Work — AI-First, Agentic by Default

Rabbit is an AI-first company. Agentic development practices are mandatory, not optional — Claude Code, Cursor, and agent-driven workflows are how we move. We measure output in shipped features, not commits.

Builders, not coders. If you’re attached to writing every line yourself, this won’t fit. If you orchestrate AI agents to compress idea-to-prototype from weeks to hours, you’ll thrive. The role is about judgment — what to build, for whom, and when it’s good enough — not implementation.

You Should Have

Must-have:

• 5+ years of software or data engineering experience — you’ve shipped real features to real users
• Deep BigQuery expertise — slots, reservations, pricing models, INFORMATION_SCHEMA, query optimization. Hard requirement; we’ll test for it. (Equivalent depth in another data warehouse plus willingness to ramp on BigQuery fast also works.)
• Strong SQL
• Comfortable in backend code (any language) — you’re not afraid to jump in when needed
• Fluent with agentic coding tools (Claude Code, Cursor, Copilot, etc.) on real production work — not just demos
• Customer-facing instinct — you can run a technical conversation with an enterprise team without an Account Executive holding your hand
• Comfort with ambiguity — you’ll often define the problem before solving it
• Autonomy — small team, no middle management, you own outcomes

Strong plus:

• GCP cost management, FinOps, or query execution planning background
• Experience taking a feature from blank page to GA, not just maintaining
• Prior Solutions Architect, TAM, or Forward Deployed Engineer experience in the data space

Why This Role

• Your ideas ship. No PoC graveyard. The optimization algorithm you prototype on Tuesday can be saving a Fortune 500 customer millions on Friday.
• Direct customer exposure. You see the impact of what you build, in dollars, on real workloads.
• Hard problems. BigQuery optimization at petabyte scale, real-time anomaly detection, cost prediction — unsolved at most companies. Not CRUD.
• End-to-end product ownership. You take features from idea to GA — shaping the problem, prototyping, validating with customers, and shipping to production. There’s a roadmap and a team behind it; your job is to own the features you take on, not to push tickets through a queue.
• AI-first leverage. You’ll work with the best agentic tools and a team that expects you to use them — your throughput compounds.

How to Apply

Send your CV or LinkedIn, plus a short note on a feature you took from idea to production — what was the customer problem, how did you validate it, and what changed for the customer when it shipped. Bonus points if you describe how you used AI agents to compress the cycle.