Data Analyst, User Operations

Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code. About the role 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. What you'll do - 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

• Show a GTM‑focused analytics portfolio: Build a short case study (1‑2 pages) that demonstrates how you turned raw support ticket data into actionable GTM insights—e.g., segmenting enterprise vs. individual users, measuring churn risk, or quantifying the impact of new feature releases on ticket volume. Include screenshots of SQL queries, data models (dbt or Looker), and the final dashboard so interviewers can see end‑to‑end execution.
• Master the stack they likely use: Cursor’s product team typically works with Snowflake/BigQuery, dbt for transformation, and Looker or Tableau for visualization. In your resume and interview, list concrete projects where you wrote performant CTE‑heavy SQL, built dbt models with tests, and delivered a self‑serve Looker Explore that reduced support triage time by ≥ 20 %. Bring a live demo or a saved Looker report to the interview.
• Quantify impact on support operations: Prepare three bullet points that translate your past analytics work into the metrics Cursor cares about—ticket resolution time, NPS for developers, and enterprise adoption rate. Use numbers (e.g., “Reduced average first‑reply time from 4 h to 2.1 h, saving 15 % of support labor cost”) to prove you can ship data‑driven improvements quickly in a flat, fast‑moving org.
• Demonstrate truth‑seeking mindset: In the interview, walk through a “failed analysis” story where you identified a data quality issue, challenged assumptions, and rebuilt the pipeline. Highlight how you documented findings, communicated them across product and engineering, and used the corrected data to drive a decision—this mirrors Cursor’s culture of spirited debate and creative problem solving.
• Negotiate with remote‑first equity benchmarks: Research recent Series B‑C remote SaaS companies (e.g., Linear, Figma) for data analyst total compensation (base ≈ $130‑$150 k + 0.05‑0.1 % equity). When you receive an offer, ask for a clear equity vesting schedule and a performance‑based bonus tied to measurable support KPIs you’ll own. Emphasize your proven impact on reducing support costs to justify a higher equity slice.