Responsibilities

• Serve as the analytical partner to US Sales leadership — present in deal reviews, forecast calls, and QBRs, surfacing prescriptive recommendations in real time.
• Diagnose pipeline health, identify deal pattern anomalies, surface rep-level productivity signals, and translate findings into recommendations leadership acts on.
• Lead quota modeling, target-setting, territory coverage analysis, and capacity modeling in support of annual and mid-year planning cycles.
• Own ICP scoring, account tiering, and enrichment-driven segmentation using Apollo, ZoomInfo, and internal frameworks.
• Analyze churn patterns, win/loss trends, and deal velocity to surface insights that cross-pollinate across Sales, Finance, and Customer Success.
• Co-own the analytical layer of core GTM products alongside GTM Engineering: Forecast Intelligence System, Pipeline Health Dashboard, Deal Scoring Engine, and territory performance models.
• Define requirements, validate data inputs, and own the business logic and insight layer. GTM Engineering owns infrastructure; you own the analytical conclusions.
• Build strategic dashboards for GTM leadership — versioned analytical products, not disposable ad hoc outputs.
• Query and validate the Snowflake data layer as the source of truth underlying these products.
• Own the accuracy of GTM data across Salesforce and integrated tools. Conduct audits, resolve discrepancies, and enforce governance standards.

Requirements

• 5+ years in revenue operations, sales operations, or a closely related analytical role within B2B SaaS.
• Strong Salesforce proficiency: reporting, dashboards, data structures, and basic admin capability.
• Excel or Google Sheets modeling proficiency — scenario analysis, not just formatting.
• Demonstrated ability to move from data to insight to recommendation. We want people who interpret data, tell a story with it, and prescribe action — not people who clean and structure it.
• Active, practical AI fluency. In the interview process, you will solve a real analytical challenge and explain how and where you used AI in your approach.
• Strong written communication: you can translate a dataset into a narrative a VP will act on.
• Exposure to quota modeling, territory design, or coverage analysis as a primary deliverable.
• Preferred: Experience with Gong, ZoomInfo, Apollo, n8n, or comparable GTM stack tools.
• Snowflake, BigQuery, or equivalent data warehouse query experience — sophisticated consumer of the data layer, not a pipeline builder.
• Familiarity with workflow automation tools and an instinct to build rather than request.