Insurance Company Data Analyst Jobs (NOW HIRING)

Data Analyst

Harper is an AI-native commercial insurance company in San Francisco. We're not bolting AI onto insurance — we're rebuilding the entire business as software, on a simple bet: turning expert human judgment into compute is one of the largest transitions left to make, and a trillion-dollar industry still run 90% by hand is the place to prove it. We've grown ~100x in the last year and we move at that speed — on-site, in person, long days, very high standards. Almost no one joins Harper for insurance; they join to build the company that replaces how it works.

The role

Harper's GTM Engineer builds the data infrastructure. You're the one who tells us what it's saying. Every morning you'll wake up asking the same question: what does the data want us to do today? You sit closest to our Growth Marketing Lead, synthesizing signal from across every channel and turning it into clear, confident recommendations they can act on. You're not a report generator — you're an analytical partner who helps drive decisions, translating the noise into the two or three things that actually matter and helping the team move on them.

You're a growth-oriented data analyst who's as comfortable drawing a conclusion as you are running the query that supports it. You've worked closely with marketing or growth teams before — not as a data service desk, but as a real partner in the work. You know what good channel performance looks like, you understand the metrics that matter in paid acquisition, and you can translate a cohort analysis into a budget recommendation without anyone having to ask. You report to the Head of Marketing, work day-to-day alongside the Growth Marketing Lead, and collaborate closely with the GTM Engineer who owns the underlying data systems. Winning in 6–12 months looks like the growth team making better decisions faster because you're in the room.

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.