Data Analyst, User Operations

The User Operations team owns the support experience for Cursor's users - from individual developers to our largest enterprise accounts. As we've scaled across regions, the volume and richness of our support data has grown faster than our ability to make sense of it. We're looking for a Data Analyst to change that.

This role sits inside our Product and Engineering organization with a dotted line to the Head of User Operations and owns the data and reporting layer for Support. You'll turn ticket data, SLA performance, customer sentiment, and product signal into the dashboards and analysis that leadership uses to run the org - and into the evidence Product and Engineering use to decide what to fix next. Support is one of the clearest signals we have about where the product is working and where it isn't. Your job is to make that signal legible.

• Build and own the reporting layer for Support: ticket volume, SLA attainment, resolution times, help center performance, CSAT/CES/sentiment, and capacity utilization across regions and tiers
• Partner with Support leadership to turn open questions - "are we getting faster but less accurate?", "where is enterprise pain actually concentrated?" - into analysis that drives decisions
• Maintain the multi-signal model behind bug and issue prioritization, weighting frequency, breadth, support cost, and sentiment, so Engineering sees a defensible picture of what impacts users most
• Design dashboards for two audiences: internal operational views for managers, and customer-facing views for enterprise accounts
• Own data quality, governance, and reliability across Support data products, including our ticketing data and internal commitments
• Surface Voice of Customer trends to Product, Engineering, and GTM, and help close the loop between what users report and what actually gets built

You may be a fit if

• You have 5+ years of experience in data analytics, analytics engineering, or a similar role
• You have strong SQL skills - this is your primary toolkit - and you're comfortable building models and transformations (dbt or equivalent)
• You've built reporting and dashboards in production that people actually depend on, not one-off charts
• You can take a vague operational question and turn it into the right analysis without a playbook
• You communicate clearly with both technical and non-technical stakeholders, and you're honest about what the data does and doesn't support
• You're self-directed and comfortable owning ambiguous problems end to end