Growth Engineer

Harper•San Francisco, CA

10h•$130,000 - $190,000•Onsite

About The Position

The Problem 36 million businesses in America need insurance—it’s not optional. 77% are underinsured. 40% have no coverage at all. The distribution system failed them: too slow, too opaque, too confusing. Over 90% of commercial insurance is still human-led. We’re building the inverse: 90%+ AI-led, pushing toward the higher 90s. Not by patching legacy workflows—by building AI that makes humans more effective, improves the customer experience, and eliminates friction at every step. We’re adding ~1,000 customers per month. We’ve grown 100x since last year. We’re looking to do even more this year—and that’s why we’re hiring. To grow that fast, we need to understand—with precision—what’s working, what’s not, and why. The Thesis The line between “analytics” and “product” barely exists here. Your models will directly power how our agents prioritize leads, time outreach, and personalize conversations. You’ll build the intelligence that tells us how well it’s working—and ship code that directly moves revenue. The Role You’ll own the metrics, analytics, and experimentation infrastructure that powers growth. This isn’t “build dashboards and wait for questions.” You’ll define the KPIs that matter, instrument systems to track them, and ship code that directly moves revenue. You work across paid acquisition channels—Google Ads, Meta, TikTok—combining it with product analytics and using AI to surface insights that would take others weeks to find.

Requirements

2-5 years experience in analytics engineering or growth engineering
Strong Python and SQL skills
Experience with paid acquisition data and funnel analytics
Ability to ship production code, not just analysis
Experience with A/B testing and experimentation
Based in San Francisco or willing to relocate

Nice To Haves

Experience with PostHog, Amplitude, or similar product analytics
Background in lead scoring or attribution modeling
Prior startup or high-growth company experience

Responsibilities

Build metrics and KPI infrastructure — Define, instrument, and own the metrics that matter in real time
Own LTV/CAC systems — Track unit economics across verticals, channels, and cohorts
Build paid channel analytics — Connect ad spend to actual revenue, not vanity metrics
Create attribution that works — Multi-touch attribution across voice, email, web, and referral
Use AI for insight generation — Pattern detection, anomaly detection, automated analysis
Ship experimentation infrastructure — A/B testing with statistical rigor