Cursor
Full Stack Analyst, GTM
San Francisco (Remote)fulltimemidAdded today
About this role
Cursor is hiring a Full Stack Analyst for their GTM team to build data infrastructure and derive insights that drive revenue decisions. As one of the first hires on this new analytics team, you'll own data models, pipelines, and analyses while establishing how the GTM organization uses data in an AI-first way.
What you'll do
- Build and maintain production GTM data models and pipelines with high quality standards
- Conduct deep-dive analyses on revenue drivers including funnel conversion, segment performance, and rep productivity
- Develop and optimize forecasting, quota, and capacity planning models for leadership
- Create self-serve analytics and governed dashboards that enable non-technical GTM stakeholders
- Partner with product Data and Enterprise Engineering teams to ensure consistent data definitions and access
- Define GTM's data strategy in an AI-first context, balancing self-serve analytics with prebuilt applications
What they're looking for
- Advanced SQL and complex dataset analysis
- Production data pipeline design and maintenance
- Financial and capacity modeling in code and spreadsheets
- GTM, revenue, or sales analytics domain knowledge
- Data visualization and stakeholder communication
- CRM and sales systems data architecture
- Forecasting and statistical modeling
- Semantic layer and data governance design
Opens the official application on the employer’s site. No login required.
Cursor
Cursor builds an AI-driven code editor used by millions of developers to transform how software is built. The company is hiring for infrastructure engineers, ML systems specialists, enterprise platform builders, security engineers, and customer success roles focused on driving adoption within large organizations.
- Website
- cursor.com
Likely interview questions
- Walk us through a time you built a production data pipeline from scratch—what was complex about it, and how did you ensure data quality?
- Describe your experience with forecasting or quota models—how did you validate assumptions and iterate based on actual results?