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San Francisco, California, United States
Onsite
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.
Data is only valuable if people can use it — and most teams can't, because the gap between raw data and actionable signal is filled with manual work nobody has time for. You close that gap. You build the automations, agents, and AI-powered workflows that take the outputs of Harper's data systems and make them legible, timely, and immediately actionable for everyone on the team. You'll spend real time in AI tools — building agents, designing prompts, orchestrating workflows — and you treat automation not as a nice-to-have but as the only way to scale a high-velocity growth operation.
You're a builder who lives at the intersection of data, AI, and automation. You're not a data engineer building pipelines from scratch, and you're not a data analyst running queries — you're the person who takes what's already flowing and makes it work for the humans who need it. You're fluent in AI tools and modern automation platforms, comfortable writing code when the situation calls for it, and you have a builder's instinct for the workflows that should never require human effort. You report to the Head of Marketing and collaborate across the GTM, growth, and operations teams. Winning in 6–12 months looks like the whole team moving faster because you built the systems that remove friction from data at every step.
Build data automations. Design and implement automated workflows that surface insights, trigger alerts, and route information to the right people at the right time — without anyone having to ask.
Develop AI agents. Use modern AI tooling to build agents that monitor, summarize, and interpret data outputs, cutting the manual work required to turn data into action.
Create operational dashboards and feeds. Build lightweight, automated reporting that gives the team an always-current view of performance — no manual refresh required.
Identify and eliminate manual data work. Audit how the team interacts with data today, find the repetitive manual steps, and replace them with automated systems.
Maintain and improve automation infrastructure. Own the reliability and performance of what you build, and iterate as data systems and team needs evolve.
Partner with the data and growth teams. Work closely with the GTM Engineer and Data Analyst to understand what data exists and where automation adds the most leverage.
You've built automations that genuinely changed how a team operates — not just connected two tools together.
You're fluent in at least one major AI development environment and have built real agents or AI-powered workflows, not just prototypes.
You have a builder's instinct: when you see a manual process, your first thought is how to eliminate it.
You're comfortable writing Python or working with APIs when no-code tools reach their limits.
You think about the end user of every automation you build — not just whether it works, but whether it's actually useful.
You can move from idea to working prototype quickly, and you know when to polish and when to ship.
You're based in San Francisco or willing to relocate.
This is a builder's seat, measured by how much faster you make the team — not by automations shipped or tools connected. It's on-site in San Francisco, in person, long days, high standards. The data systems and team needs keep changing underneath you, which means the automations you own have to keep working, and when one breaks or goes stale, that's yours to find and fix — usually the same day. You'll work open-ended problems with little spec, so the job is as much judgment about what's worth automating as it is the building itself.
This isn't a place to wire up integrations no one uses, and it isn't a pipeline-engineering or pure-analyst role. Almost no one takes it for insurance — they take it because building the AI-powered automation layer for a company growing ~100x, where automation is a real competitive advantage, is a rare seat. If making a team measurably faster is the appeal, apply. If it's a deterrent, it won't get easier here.
Salary: $120,000–$160,000, 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.
Uber commuter benefits
Meals provided — breakfast, lunch, and dinner
Snacks, drinks, and coffee stocked daily
Free gym membership
Health, dental, and vision insurance
3+ years of experience in automation, data operations, or a technical marketing/growth operations role
Hands-on experience building workflows in automation platforms (Zapier, Make, n8n, or comparable)
Demonstrated experience building with AI tools and LLM APIs to create agents, summaries, or AI-powered workflows
Working knowledge of Python or another scripting language for tasks that require custom logic
Familiarity with data outputs from marketing and analytics platforms; comfortable reading and transforming structured data
Strong problem-solving instincts and the ability to work autonomously on open-ended technical challenges
Experience building agents using modern AI frameworks (LangChain, CrewAI, or comparable)
Background working alongside data engineers or analysts to understand and extend data pipelines
Familiarity with marketing analytics stacks and GTM data flows
Experience with prompt engineering and workflow design for LLM-based systems
Exposure to a high-growth startup environment where automation was a competitive advantage
People screen — fit and alignment
Lead screen — skills and judgment
Super day — how you operate in real time
Send your resume and a description of an automation or AI agent you built — the problem it solved, and how you built it.
