Marketing & GTM Data Analyst

Minimum Skills, Knowledge & Capabilities

• 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 Qualifications (nice-to-have, not required)

• 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.