Analytics Engineer

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.

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.

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.

Big Query (warehouse)
· Coalesce (transformation pipelines)
· Sigma (reporting)
· Castor (catalog)
· Python
· Git-based workflows. Domain systems vary — billing (Orb), CRM, Post Hog, Rippling, Ashby, cloud marketplaces.

• 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 Big Query 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.

• Finance — the order-to‑cash…