Data Analyst, User Operations

Cursor; United States; Remote; USD 80,000 - 110,000; Full time

Job summary

Cursor is looking for a Data Analyst to enhance the support experience for users. The role focuses on building reporting and dashboards to analyze ticket data, SLA performance, and customer sentiment.

The ideal candidate will have 5+ years of experience in data analytics, strong SQL skills, and a track record of building operational reporting in production. Join a small, dedicated team passionate about automating coding.

Qualifications

• 5+ years of experience in data analytics, analytics engineering, or a similar role.
• Strong SQL skills and experience building models and transformations.
• Experience in building reporting and dashboards in production.

Responsibilities

• Build and own the reporting layer for Support.
• Partner with Support leadership to drive decisions through analysis.
• Maintain multi-signal model for bug and issue prioritization.
• Design dashboards for internal and customer-facing audiences.
• Own data quality, governance, and reliability across support data products.
• Surface Voice of Customer trends to relevant teams.

Skills

SQL skills

Data analytics

Dashboard building

Analytical problem-solving

Tools

dbt or equivalent

Job description

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