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Onsite or remote • Budapest+3💼 Full-time🗓 2026-06-25

Core

Product Engineer responsible for end-to-end feature development and customer success for Rabbit's BigQuery cost optimization platform.

Role type

Product Engineer (BigQuery optimization)

Builds

Production-grade pipelines, APIs, and infrastructure-as-code for BigQuery cost optimization.

Domain

Cloud Data Warehousing (BigQuery) / Cost Optimization

Deliverable

production ML models | product features | infrastructure

Required skills

BigQuery expertise (slots, reservations, pricing, query optimization), SQL, backend development, agentic coding tools (Claude Code, Cursor), customer-facing technical communication, problem definition in ambiguity

Preferred skills

Experience with other data warehouses, GCP ecosystem knowledge

Technologies

BigQuery, GCP, Claude Code, Cursor, Copilot

Responsibilities

Ship features end-to-end from prototype to GA, run customer pilots and gather feedback, drive adoption and troubleshoot enterprise customers, investigate data anomalies and cost questions, feed field signal into product direction

Seniority

Mid-Senior, hands-on IC

Rewrite
## About the Role At Rabbit (an innovative product by Aliz), 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: - 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. - 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
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