Marketing & GTM Data Analyst

Responsibilities

• Automate recurring GTM analytics with Decision Intelligence, designing and improving AI agents for weekly reporting, campaign and sales play readouts, scoring-model checks, and anomaly alerts, as the GTM expert who writes evaluation cases and QAs their output.
• Own Marketing and GTM KPI definitions, calculation logic, and the weekly report leadership reads, with a clear narrative on what changed and why.
• Measure marketing campaigns and SDR/AE sales plays from first touch through closed-won, using pre/post and cohort analysis to say what to scale, fix, or stop.
• Create reusable campaign and sales play reporting templates with consistent definitions, time windows, and attribution rules, structured so AI agents can generate first drafts.
• Own Dreamdata and evolve our attribution practices (multi-touch, sourcing rules, SDR credit, partner attribution), reconcile them with Salesforce and HubSpot, and turn attribution into budget recommendations.
• Evaluate and improve MQL, PQL, and account intent scoring models, with automated monitoring of precision, coverage, and conversion lift by score.
• Define and report partner and business development performance, including partner-sourced and influenced pipeline, co-marketing results, and performance by tier.
• Define GTM source-of-truth datasets with Data Engineering and Decision Intelligence, write the semantic context AI analytics agents rely on, and drive data quality fixes upstream.

Requirements

• Turns vague or complex requests ("did re:Invent work?", "why is pipeline soft?") into clear, answerable questions before touching data, and pushes back constructively when the question is wrong.
• Understands the B2B funnel (MQL, SAL, SQL, Closed Won) and how marketing, product-led, partner, SDR, and AE motions interact, and explains why the numbers look the way they do.
• Writes strong SQL directly in a warehouse and across Salesforce objects (Leads, Contacts, Campaigns, Campaign Members, Opportunities, custom objects).
• Reconciles messy data across systems, holds a high bar for what counts as right, and fixes problems at the source rather than patching downstream.
• Has owned or improved a multi-touch attribution setup and reconciled it with CRM data.
• Evaluates or tunes lead, PQL, or account scoring models and measures whether they improve conversion.
• Operates AI-native: builds AI agents or agentic workflows for analytics (e.g., Claude Code, MCP servers, n8n), automates recurring analysis, and writes the evaluation cases and context agents need to be accurate.
• Builds dashboards and recurring reports in a BI tool that people rely on and act on, with a narrative sized for leadership.
• Owns KPI definitions so the same number means the same thing everywhere.

Preferred

• Typically ~3–5 years in marketing analytics, GTM or revenue analytics, or a similar analytical role at a B2B SaaS company, or equivalent demonstrated experience.
• Hands-on with Dreamdata, HockeyStack, Bizible, or a similar attribution tool, plus HubSpot and Salesforce reporting.
• Python, and experience with dbt, semantic layers, or metric stores.
• Worked with intent data or partner attribution.
• Experience at a usage-based, developer-focused, or product-led business.