Member of Technical Staff, Data / Context Engineer

Join us if you want to change how a $12 Trillion market is serviced and lead the industrial AI revolution.

We are building the first professional services factory in history. For centuries, professional services have operated on a fundamentally human-centric model. We are here to change that.

Commercial insurance is a $1 trillion market serving 36 million businesses in the United States and its broken. Commercial insurance is broken not because of insufficient capital or demand—it's broken because distribution scales linearly and quality differs by the servicer. Traditional brokers need more people, more training, more locations. We're replacing that model with computational systems.

Old model: Hire humans → Train them → Scale linearly

Our model: Build computational systems → Humans teach edge cases → Scale exponentially

Professional services represent a $12 trillion market in the US. Insurance brokering is our entry point to proving that AI-native (business) models can replace human-centric expertise with computational systems that scale exponentially. If we win here, we change how every knowledge work industry operates.

We've grown 100x in 12 months—from $4K to $500K monthly ARR. Our sales reps hit 30x the industry average. Not through better training, but through computational systems.

What You'll Do

Build Pipelines. Model the Database(s). Expose Context as Services.

• Build distributed, scalable, and reliable ETL pipelines using Airflow, Temporal.io, and n8n.
• Build services to “broadcast” information (updates / changes) to be consumed by downstream services.
• Model schemas for - SQL database(s), Vector database(s), and NoSQL database(s).
• Build custom automations using Playwright, Stagehand, Zapier, and more.
• Work with AI Researchers and Engineers, and model “Context” - Information packets required by agents and workflows as input. Expose them as services.
• Understand the business and advocate for changes to the actual business process if that improves the data quality / collection.
• Identify gaps in data and inform where processes need to be changed to improve data (and therefore operations).
  
  

What We're Looking For

Data Engineers who can build frontend. Engineers who love the grind, can serve prototypes and build iteratively for scale. We love potential / ex founders - people who’re willing to jump through any hoops, think out of the box, and do anything to achieve the objective. We believe that great engineers are the ones who can identify and fix flaws in the entire process, not just (try) to fix it with code.

You'll Thrive Here If You Have:

• 2 years shipping fast in startup environments (0→1 experience)
• Proficiency in data modeling for SQL database (Postgres) and Vector database (Qdrant)
• Experience building API / function services in Python
• Experience building distributed systems
• Experience building pipelines using Airflow, Temporal.io, n8n
• Experience building stream processing application using Flink
• Founder mindset: previous founder, founding team member, or deep startup exposure
  
  

Nice to Have:

• Experience fine-tuning prompts and architecting context
• Experience with transforming and warehousing data using dbt
• Experience with OLAP databases like Clickhouse
• Experience building CDC pipelines to live hydrate data warehouse
• Experience with analytical tools like Posthog
  
  

We operate with:

• Extreme Pace: Ship 80% solutions in days, not weeks
• Impeccable Taste: Know when simple beats complex and when to optimize
• Competitive Fire: Here to win, not participate
• Ownership: Take responsibility for outcomes, not deliverables
• Day Zero Mindset: Throw away your work if data says it's wrong
  
  

The Harper Philosophy

• "There is no try, there is just do" - Ship fast, iterate. Analysis paralysis is failure.
• "Actions lead to information" - Do something and measure. Don't wait for perfect data. Actions generate data which help action better.
• "Strong opinions, loosely held" - Argue passionately, but change completely when data says so.
• "Software updates, not training updates" - Insights should improve systems automatically.
  
  

Why Join Harper

• Real AI in Production: See how AI actually works at scale (95% of companies never get here). We’re running a real business but AI native.
• Impact at Scale: Help prove that professional services can be computational. If we win in insurance, we change legal, accounting, HR—every knowledge worker industry.
• Founder Training Ground: 12 months here = 3-5 years of learning elsewhere. Know what it means to be an operator.
  
  

Compensation & Perks

💰 Compensation: Competitive base equity

🍲 Meals: Lunch / Dinner covered

🏥 Health: Medical, dental, vision

This role is NOT for you If:

• You want 9-5 hours, clear scope, or work-life balance
• You need perfect data before taking action
• You prefer reporting over ownership
• You're not comfortable with hypergrowth intensity
  
  

Interview Process:

• 30-min screening call
• Take-home coding assessment
• On-site
  
  

We move fast: Application to offer in 1-2 weeks for the right person.

The Mission

We're building the first professional services factory. Professional services = $12 trillion in US annual labor. The winning model will be AI-native from day 1.

The intelligence you build demonstrates that the Industrial Revolution for knowledge work is possible. If that mission excites you, if you're ready for intensity and opportunity—we should talk.

Compensation Range: $140K - $200K