Senior GTM Analytics Engineer

Cursor

San Francisco (CA)

On-site

USD 140,000 - 200,000

Full time

14 days+

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Job summary

Cursor is hiring a hands‑on GTM Analytics role. You’ll own the GTM data models and pipelines that drive revenue analytics, build a trustworthy semantic layer, and collaborate with GTM Apps, RevOps, and product teams to ensure consistent data across dashboards and applications.

You’ll run analyses on funnel conversion, segment performance, customer success, and rep productivity, and you’ll model forecasting, quota, and capacity to support leadership decisions in an AI‑first data environment.

Qualifications

  • Your SQL is exceptional (non‑negotiable).
  • You've built and maintained production data pipelines and models, CRM and GTM systems included.
  • You've built forecasting, quota, or capacity models.
  • You can operationalize metrics and tooling for non‑technical stakeholders.

Responsibilities

  • Own the GTM data models and pipelines powering analysis; maintain high quality standards and a safe semantic layer.
  • Run deep‑dive analyses on revenue drivers: funnel, segment performance, customer success, rep productivity.
  • Optimize forecasting, quota, and capacity models and test underlying assumptions.
  • Define AI‑first data interactions for self‑serve vs governed dashboards and apps.
  • Partner with product Data and Enterprise Engineering to ensure consistent pipelines and definitions.

Skills

SQL
Production data pipelines
Forecasting models
Operationalize metrics
Analytical judgment
GTM domain experience

Job description

Cursor is hiring a hands‑on GTM Analytics role. You’ll own the GTM data models and pipelines that drive revenue analytics, build a trustworthy semantic layer, and collaborate with GTM Apps, RevOps, and product teams to ensure consistent data across dashboards and applications.

You’ll run analyses on funnel conversion, segment performance, customer success, and rep productivity, and you’ll model forecasting, quota, and capacity to support leadership decisions in an AI‑first data environment.

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