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Fireworks is a Series D AI infrastructure platform ($17.5B valuation) that enables companies to build, train, and serve AI models tailored to their data, workflows, and products. Founded by the PyTorch team and backed by AMD, NVIDIA, Sequoia, Benchmark, and others, Fireworks powers production AI across text, image, embedding, audio, and multimodal workloads.
You will be an Analytics Engineer embedded within one of four business functions: Finance, Product-Led Growth, People, or Partnerships. The data organization operates as a hub-and-spoke model. The central Data Platform team owns company-wide infrastructure, modeling standards, data quality, governance, and developer experience. You report to Data Platform but sit with your function, join its planning, and spend most of your time answering its questions with data.
This role offers genuine ownership of a domain plus a real platform underneath. You own the analytics roadmap and execution for your function and advocate its priorities back to the central team. You are not a ticket queue; your models reconcile to the same certified definitions everyone uses.
Key responsibilities:
- Own your function's data domain end to end: define what should be measured, model it, and be accountable for the numbers.
- Build and maintain pipelines in BigQuery and Coalesce, aligned with company data standards and governance.
- Establish authoritative models for your domain that reconcile against certified account, usage, and revenue definitions.
- Build dashboards your team runs the business on, trustworthy enough to replace spreadsheet exports.
- Contribute back to the platform: improvements you make land for everyone.
- Surface problems before they're asked about—variance, anomalies, mix shifts, completeness gaps.
- Automate the manual: build agentic and LLM-powered workflows for reconciliation, anomaly detection, routine reporting, and operational handoffs.
Domains hiring for:
- Finance: order-to-cash pipeline, usage metering, invoicing, revenue recognition, reconciliation, board reporting.
- Product-Led Growth: self-serve funnel from docs through signup, first API call, activation, sustained paid usage, plus instrumentation and experimentation.
- People: workforce intelligence and automation across the employee lifecycle, built on HR and recruiting systems.
- Partnerships: partner economics and revenue share with AI labs, tied to billing records and audit-ready.
Tech stack: BigQuery (warehouse), Coalesce (transformation), Sigma (reporting), Castor (catalog), Python, Git-based workflows. Domain systems vary: Orb (billing), CRM, PostHog, Rippling, Ashby, cloud marketplaces.
REQUIREMENTS:
Required for all roles:
- 5+ years in data analysis, analytics engineering, and/or data engineering
- Strong SQL proficiency: design good schemas, write your own queries, optimize warehouse usage
- Strong visualization proficiency: build dashboards your team can leverage for efficient decision-making
- Working knowledge of data engineering: pull requests, code review, Git workflows
- Strong analytical thinking: decompose ambiguous problems, find root causes, turn maybes into defensible explanations
- Clear communication with non-technical partners: translate system logic into actionable terms
- Bias toward building systems over managing processes: treat recurring manual work as a problem to solve
Preferred:
- Python proficiency for automation, data work, and integrations
- Experience building LLM-powered agents or automation workflows on top of analytics
- Experience with modern BI and transformation tooling (Sigma, Looker, Tableau; dbt, Coalesce, or similar)
- Background in AI infrastructure, developer tools, or usage-based platforms (token pricing, GPU-hour metering, model mix, cost-per-request)
- Depth in one of the domains: billing/revenue systems, product/web analytics/experimentation, HRIS/ATS/workflow automation, or partner/marketplace economics
