Role Overview

We are seeking a Senior AI Data Analyst to join our GTM AI & Analytics Team. In this role, you will own the data strategy that powers our global Sales organization — helping us understand pipeline health, sharpen forecasting, drive our sales planning cycles (capacity, quota, territory, and headcount), and grow pipeline generation, conversion, and revenue across complex enterprise accounts and regions.

You'll operate at the intersection of analytics, data engineering, and business strategy, partnering with Sales leadership, RevOps, Marketing, and Finance to ensure decisions are grounded in trusted, scalable data.

This is an AI-forward role. We are already actively building the next generation of GTM analytics — where trusted definitions and version-controlled analytical "skills" let people ask questions to get answers on live data. You won't be experimenting with AI from scratch; you'll be extending a program that is already in production and helping scale it across the Sales org.

Leading with AI

AI is not a side project here, it's how our GTM AI & Analytics team already works. You will contribute to and scale GTM Cortex, our GTM analytics knowledge and orchestration layer:

• Author analytical "skills" as code — small, testable, version-controlled skills (atomic to composite) that run governed analyses on live data, maintained in our GitHub repo.
• Work in an AI-native workflow — author and iterate skills in Claude Code, querying live Pigment data safely through the Pigment MCP (our standard, governed way to query data with AI).
• Ground everything in a single source of truth — reference canonical metric and dimension definitions from our KPI Catalog rather than redefining logic ad hoc.
• Push adoption of AI-driven, self-service analytics across Sales — turning recurring questions ("why did win rate drop?", "where is pipeline coverage weakest?") into reusable skills instead of one-off analyses.
• Drive internal usage and adoption of our Analyst and Modeler AI agents within the Sales organization.

Key Responsibilities

Executive and Operational Analytics

• Own Sales executive dashboards: pipeline generation, pipeline coverage, forecast, win rates, sales cycle, quota attainment, ARR, and net-new vs. expansion
• Build advanced Pigment models and opportunity-level datasets for pipeline, capacity, and revenue analysis (including sales capacity and productivity modeling by region and segment)
• Standardize enterprise Sales metrics and definitions across teams and regions

Planning and Capacity Modeling

• Serve as the analytics backbone of our Sales planning cycles — annual and in-year — partnering closely with Sales leadership, RevOps, and Finance
• Build and maintain capacity, quota, and productivity models in Pigment
• Model headcount and territory scenarios, translating go-to-market strategy into bottoms-up pipeline and revenue targets
• Pressure-test planning assumptions against historical performance and run what-if scenarios to support key GTM decisions

Data Architecture and Automation

• Design and maintain scalable data pipelines from Salesforce, Pigment, Gong, Netsuite and other tools in our GTM stack
• Partner with Data Engineering on transformations (dbt, warehouse modeling)
• Build and scale AI skills on top of the Pigment MCP so analyses are reusable, governed, and automatable (recurring reports, event-driven alerts)
• Ensure data quality, governance, and documentation for all Sales data assets

Strategic Business Partnership

• Act as the analytics partner to Sales leadership and regional sales orgs
• Answer high-impact questions like:
  • What is driving changes in win rate and sales cycle?
  • Where is pipeline coverage weakest, and is capacity aligned to target?
  • How does pipeline generation quality correlate to closed-won outcomes?

Enablement and Self-Service

• Build role-based reporting for Reps, Managers, and Executives
• Train Sales teams to use data in pipeline reviews, QBRs, forecast calls, and account planning
• Drive adoption of AI-driven, insight-first workflows across the Sales organization

Required Qualifications

• 5–8+ years in Data Analytics, Analytics Engineering, or Business Intelligence
• Advanced SQL and experience with enterprise-scale data warehouses (Snowflake, BigQuery, Redshift)
• Strong experience with SaaS and sales metrics at scale (ARR, pipeline generation, coverage, win rate, quota attainment, forecast accuracy)
• Hands-on experience with BI/planning tools (Pigment, Looker, Tableau, Power BI, Mode, etc.)
• Experience supporting enterprise Sales or RevOps teams, including GTM planning (capacity, quota, territory, or headcount modeling)
• Genuine enthusiasm for working in an AI-native analytics environment (LLM-assisted analysis, skills-as-code, governed data access)

Nice-to-Have

• Pigment experience
• Experience with Salesforce, Gong, Clari, Outreach/Salesloft, Segment, Amplitude, or similar
• Hands-on experience with AI coding/analysis tools (e.g. Claude Code) and/or MCP-based data access
• dbt, Python, or analytics engineering background
• Experience in B2B enterprise SaaS environments
• Familiarity with complex, multi-segment sales and forecasting motions

What Success Looks Like

• Sales leadership relies on your insights for pipeline, forecast, and capacity planning
• Pipeline and forecast risks are visible early and explained, not just reported
• Reps and managers use your dashboards and AI skills in every pipeline review and QBR
• A growing library of trusted, reusable Sales analytics skills reduces one-off requests
• Planning cycles run on your models — capacity, quota, territory, and headcount decisions are grounded in trusted scenarios
• Data is trusted, consistent, and embedded in Sales workflows