Analytics Engineer

ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. ABOUT US Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. THE ROLE We're hiring several Analytics Engineers to embed with the functions that run the business — Finance, Product-Led Growth, People, and Partnerships. Our data org runs as a hub and spoke model. The central Data Platform team is the hub: it owns company-wide data infrastructure, modeling standards, data quality, reliability and observability, governance, and the developer experience everyone else builds on. You would be a spoke. You report into Data Platform, but you sit with your function, join its planning, and spend most of your time answering its questions with data — and the remainder engineering on top of the platform's shared infrastructure and standards. That means you get both things analytics engineers usually have to choose between: real ownership of a domain, and a real platform underneath you. You own the analytics roadmap and execution for your function and you're the voice of its priorities back to the central team. You are not a ticket queue, and you are not building a private stack in a corner — your models reconcile to the same certified definitions everyone else uses. THE STACK BigQuery (warehouse) · Coalesce (transformation pipelines) · Sigma (reporting) · Castor (catalog) · Python · Git-based workflows. Domain systems vary — billing (Orb), CRM, PostHog, Rippling, Ashby, cloud marketplaces. WHAT YOU'LL DO - Own your function's data domain end to end — define what should be measured, model it, and be accountable for the numbers when someone asks where they came from. - Build and maintain pipelines in BigQuery and Coalesce, aligned with company data standards, certified definitions, and governance requirements. - Establish authoritative models for your domain that reconcile against the company's certified account, usage, and revenue definitions — not a second set of numbers. - Build the dashboards your team runs the business on, and make them trustworthy enough to replace spreadsheet exports and one-off pulls. - Contribute back to the platform — the frameworks, standards, and tooling you use are shared, and improvements you make land for everyone. - Surface problems before they're asked about — variance, anomalies, mix shifts, and completeness gaps should reach your stakeholders from you first. - Automate the manual. Once the core data is hardened, build the agentic and LLM-powered workflows that take the repetitive work off your team's plate — reconciliation, anomaly detection, routine reporting, operational handoffs. DOMAINS WE'RE HIRING FOR - Finance — the order-to-cash pipeline end to end: usage metering, invoicing, revenue recognition, reconciliation, and the reporting that monthly close and board updates run on. - Product-Led Growth — the self-serve funnel from a developer reading our docs through signup, first API call, activation, and sustained paid usage, plus the instrumentation and experimentation behind it. - People — workforce intelligence and automation across the employee lifecycle, built on our HR and recruiting systems and handled with the sensitivity people data requires. - Partnerships — partner economics and revenue share with leading AI labs: numbers that tie to billing records, pass audit, and partners trust. The technical bar is the same across all four. Apply once and tell us which domain fits you — if more than one appeals, say so and we'll figure it out in the first conversation. WHAT WE'RE LOOKING FOR Required — all roles - 5+ years in data analysis, analytics engineering, and/or data engineering - Strong SQL proficiency — you can design a good schema, write your own queries, and not bog down the warehouse - Strong visualization proficiency — you can build your team the dashboards they need so they can leverage the data for efficient decis…