GTM Engineer

The Problem

36 million businesses in America need insurance—it’s not optional.
77% are underinsured. 40% have no coverage  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 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.

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, Tik Tok—combining it with product analytics and using AI to surface insights that would take others weeks to find.

• 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

• You ship code—production code, not just notebooks (Python, SQL)

• You’ve defined KPIs, built instrumentation, and been accountable for moving them

• You’ve worked with paid acquisition data (Google Ads, Meta, Tik Tok)

• You use AI to accelerate analysis (Post Hog or similar)

• You’ve built GTM systems: lead scoring, attribution, LTV/CAC analysis

• You think in experiments and know correlation vs. causation

• You’re 2-5 years into your career

• 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

• Experience with Post Hog, Amplitude, or similar product analytics

• Background in lead scoring or attribution modeling

• Prior startup or high-growth company experience

• Salary: $130,000–$190,000 + performance bonuses & equity

• Location: San Francisco, in-office

• Health, dental, and vision insurance

• Commuter benefits

• Team meals and snacks

Data talks. Narratives walk. If you prove things instead of just believing them—send your resume and an example of analysis that drove a business decision.