Data Analyst

What you'll do - Synthesize channel performance. Pull together data across paid, organic, and partner channels - and identify what's working, what's not, and what to do about it. - Partner with the Growth Marketing Lead. Serve as their analytical right hand; translate data outputs into clear recommendations they can act on in real time. - Own growth KPI reporting.

Define, maintain, and communicate core marketing and funnel metrics so the team is always looking at the right numbers. - Build and maintain LTV/CAC analysis. Give leadership a clear, current picture of unit economics by channel, cohort, and customer segment. - Run experiment analysis. Evaluate A/B tests and campaign experiments - and turn results into decisions, not slide decks. - Surface what the data is asking for.

Proactively flag trends, anomalies, and opportunities before anyone has to ask you to look. Who you are - You've worked as an analytical partner to a marketing or growth team - not just supported them from a distance. - You can draw a clear conclusion from messy data and defend it in a room with people who will push back. - You think in recommendations, not just findings - you're not done until you've said "and therefore we should..." - You're fast: you can turn a question into an answer in hours, not days. - You understand paid acquisition channels well enough to have opinions about where the budget should go. - You thrive in a high-tempo environment where the questions change faster than the data does. - You're based in San Francisco or willing to relocate.

The reality - read this before you apply This is a decision-driving seat, measured by the decisions your analysis changes - not dashboards shipped or tickets closed. It's on-site in San Francisco, in person, long days, high standards. The questions move fast and the data is messy, which means you'll be expected to draw a conclusion and stand behind it, often before the picture is fully clean.

You're the analytical engine for the growth team, so when a number looks wrong or a channel turns, finding out why is yours - frequently the same day. This is explicitly not a data service desk, and it isn't a place to produce reports no one acts on. Almost no one takes it for insurance - they take it because being the analytical right hand to a growth team at a company scaling ~100x, with a direct line from your recommendation to where the budget goes, is a rare seat.

If being measured on decisions is the appeal, apply. If it's a deterrent, it won't get easier here.

Compensation & logistics - Salary: $140,000-$180,000, plus performance bonus and equity. - Location: On-site, San Francisco. Based here or willing to relocate. - Schedule: Monday-Friday, long days, in the building with the team.

Benefits - Uber commuter

benefits - Meals provided - breakfast, lunch, and dinner - Snacks, drinks, and coffee stocked daily - Free gym membership - Health, dental, and vision insurance

Requirements - 4+ years of analytics experience with direct exposure to marketing, growth, or revenue teams - Strong SQL; Python proficiency preferred - Hands-on experience with funnel analysis, cohort analysis, attribution, and LTV/CAC modeling - Experience analyzing paid acquisition channel performance (Google, Meta, or comparable) - Proven ability to communicate analytical findings clearly to non-technical stakeholders and drive decisions - Experience partnering with a marketing or growth counterpart to act on data in real time - not just produce reports Nice to have - Familiarity with PostHog, Mixpanel, Amplitude, or comparable product analytics platforms - Experience working alongside a data engineer or GTM engineer and understanding how data pipelines work - Background in insurance, fintech, or another industry with complex funnel and attribution dynamics - Exposure to AI-assisted analytics workflows and modern BI tooling - Experience in a high-growth startup environment where speed and adaptability were required Process - People screen - fit and alignment - Lead screen - skills and judgment - Super day - how you operate in real time To apply Send your resume and an example of analysis you ran - what you recommended, and what the team did with it.