Fireworks AI·San Mateo·3 дн назад
Analytics Engineer
Ориентир по рынку≈ 717 000 – 2 336 000 ₽наша приблизительная оценка по 8 вакансиям этой роли и грейда, у работодателя вилка не указана
🏢 ОфисMiddleПолная занятость
Вилки нет, про деньги придётся договариваться с нуля.
Вакансия на английскомПереведёт заголовок и описание вакансии на русский
Наша компания
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.
Чем предстоит заниматься
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
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
Наши требования
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
Дополнительно
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
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
WHY FIREWORKS?
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
Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators
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