Analytics Engineer at Fireworks AI in San Mateo

Job description

• Application
• 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.

Responsibilities

• 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.

Requirements

• 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 decision making
• Working knowledge of data engineering — you already know how to put up a pull request, respond to code review feedback, etc.
• Strong analytical thinking — you decompose ambiguous problems, find the root cause behind a number that moved, and turn a maybe into a defensible explanation
• Clear communication with non-technical partners — you can translate system logic into terms your stakeholders can act on
• A bias toward building systems over managing processes — you treat recurring manual work as a problem to solve
• Proficiency in Python for automation, data work, and integrations
• Experience building LLM-powered agents or automation workflows on top of analytics — after the core numbers are hardened
• 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, and cost-per-request as first-class business metrics
• Depth in one of the domains above — billing and revenue systems, product and web analytics and experimentation, HRIS/ATS platforms and workflow automation, or partner and marketplace economics
• We don't expect every bullet. Tell us which ones you have.

Skills

Required

• Strong SQL proficiency
• Strong visualization proficiency
• Working knowledge of data engineering
• Strong analytical thinking

Preferred

• Depth in one of the domains above

Benefits

• Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
• Build What's Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
• Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI — no bureaucracy, just results. You'll own your function's data and automation infrastructure, not sit in a ticket queue.
• Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.
• Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
• Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
• Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
• Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.