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Data Analyst, User Operations

Join Cursor as a Data Analyst to enhance user support through data-driven insights and reporting.

Location
Remote
Compensation
Not disclosed
Level
mid
Type
full time

Skills & Technologies

dbt SQL Flexible on stack

Requirements

Experience
5+ years

Joblaze summary

In this role, the Data Analyst for User Operations at Cursor focuses on transforming support data into actionable insights that guide product and engineering decisions. Proficiency in SQL and experience with data modeling and dashboard creation are essential, as the analyst will manage reporting on ticket performance, customer sentiment, and operational efficiency. This position is ideal for someone with over five years of analytics experience who thrives in a collaborative, fast-paced environment and can navigate ambiguous challenges independently. Cursor's flat organizational structure encourages creativity and open dialogue, making it a dynamic place for innovative thinkers.

Joblaze insights

Quick facts

How much experience is required?
At least 5 years of relevant experience for this Data Analyst, User Operations role.
What's the tech stack?
Joblaze extracted these technologies from the posting: dbt, SQL.
What seniority level is this role?
Cursor targets mid-level candidates for this position.
Is this full-time or contract?
Full-time for this Data Analyst, User Operations role at Cursor.

From the original posting

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

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