Revenue Analytics Lead, Fireworks AI

• 7+ years in revenue operations, GTM analytics, or business intelligence at a high-growth B2B company; 2+ years in a lead or senior IC role
• Advanced SQL and hands-on Snowflake and dbt experience — not just familiarity
• Proficient in Python or R for statistical modeling and analysis
• Deep Salesforce knowledge: objects, fields, relationships, CRM-to-warehouse translation
• Has owned GTM planning cycles end-to-end: quota-setting, territory design, capacity modeling
• Actively using AI tools in current workflow; able to name specific automation they've built

Desirable

• Reporting to the Director of Revenue Strategy & Analytics, the Revenue Analytics Lead is the analytical engine of the GTM org
• This role owns the data infrastructure, predictive models, and reporting suite that empower sales reps, GTM leadership, and informs how we run the business
• The ideal candidate knows how to interface with Data Engineering to build our data foundations from the ground up, while working with GTM leadership to inform our territory strategies
• You need the depth to build governed data models, write advanced SQL, and deploy statistical forecasting models
• You also need the judgment to know which number matters and how to tell inform VPs and SVP of Sales
• This role starts as a senior IC and grows into a team lead as we scale
• The right candidate is already using AI to automate and accelerate their impact into a 10x employee
• Own the source of truth for GTM health: pipeline, bookings, win rates, sales velocity, rep productivity, GPU consumption trends, and segment performance
• Build and maintain the analytical framework across the full funnel — outbound through AE close and expansion
• Build executive dashboards that give leadership real-time visibility into performance, risk, and opportunity
• Execute annual and quarterly planning cycles: territory design, quota-setting, capacity modeling, and headcount planning in partnership with Finance and the Director
• Build and maintain predictive models for revenue pacing and sales attainment
• Own the quantitative inputs to the forecast: conversion rates, coverage ratios, pipeline aging, and ramp curves
• Build scalable, governed GTM data models in Snowflake and dbt. Own definitional standards: pipeline, logo, ARR, GPU consumption
• Maintain data integrity across Salesforce and GTM systems. One source of truth, enforced
• Deploy AI and automation workflows across GTM analytics: reporting automation, pipeline intelligence, and LLM-assisted analysis
• Automate recurring analytical work so capacity goes to interpretation, not production