Data Analyst

United States·San Franciscofull-timemid

Data AnalystData

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Quick Summary

Requirements Summary

when a manual process repeats twice, the next step is automating it, not doing it a third time. Able to go from a vague stakeholder ask to a specific, well-scoped answer without heavy hand-holding.

Technical Tools

Data AnalystData

Outset invented a better way to do research: AI-powered user interviews.

Global brands like Hubspot, Microsoft, Uber and Nestle use Outset to achieve deeper, qualitative insights about their users at unprecedented speed and scale.

The research industry is massive (>$140B and growing) and ripe for disruption. Incumbents are outdated, slow, and burdened by high-cost services. That's our opportunity.

Responsibilities

~1 min read

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Answer recurring business questions across GTM, People, Product and Engineering using SQL, Python, and whatever AI tooling gets there fastest.
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Build small internal tools - vibe coded apps, Slack bots, scripts, lightweight dashboards, AI skills - that eliminate a manual report or a repeated ad hoc request.
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Partner with stakeholders to figure out what they actually need before building anything.
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Maintain and improve data pipelines so the underlying numbers are trustworthy, not just the tools on top of them.
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Evaluate new AI tools and workflows as they come out, and decide which are actually worth adopting internally vs. hype.
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Document what you build well enough that someone else can run it without you in the room.
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Flag data quality issues and inconsistent definitions before they turn into bad decisions.

Requirements

~1 min read

3+ years in a data analyst, analytics engineer, or BI role, with real SQL and Python fluency.
Hands-on experience using AI tools (Claude, ChatGPT, Copilot, Cursor, or similar) to build something real - a script, an agent, an automation - not just to summarize text.
A builder's instinct: when a manual process repeats twice, the next step is automating it, not doing it a third time.
Able to go from a vague stakeholder ask to a specific, well-scoped answer without heavy hand-holding.
Clear written communication - can explain a finding or a tool to someone non-technical in a few sentences.

Experience at a startup where the data stack and tooling were still being built, not inherited mature.
Exposure to building or prompting AI agents/workflows (not just using chat interfaces).
Familiarity with modern BI/data tools (dbt, Looker, Hex, or similar).