Skills: Python, Redis, Kafka, Google Cloud, Elasticsearch, Distributed Systems, Data Warehousing, ETL, Elastic Stack (ELK), PostgreSQL

The Mission

We're building an AI-powered insurance brokerage that's transforming the $900 billion commercial insurance market by automating processes that currently run on pre-internet systems. Fresh off our $8M seed round, we're looking for an exceptional AI Context Engineer who can architect and develop durable, scalable data pipelines that power our AI systems with high-quality context.

We believe the best context leads to the best decisions and outcomes. You'll build data engineering pipelines that pull information from various sources and push it into memory stores for AI agents, while simultaneously feeding our data warehouses for analytics. While your primary focus will be on context engineering, you'll also have the opportunity to lean into building AI agents themselves and engage in prompt engineering. Your work will directly enable our agents to make better decisions through richer context, while providing our growth and data teams with the insights they need to drive campaigns and optimize performance.

We're committed to "Staying REAL" with our AI systems - building agents that are Reliable, Experience-focused, Accurate, and have Low latency. You will work directly with the CTO, our applied AI engineers, the CEO, growth team, and sales team to execute on our AI vision with a bias toward action. We live by core principles: "There is no try, there is just do," "Actions lead to information, always default to action," and "Strong opinions lead to information." We need engineers who build and ship, not just plan and strategize.

Outcomes You'll Drive

Build durable, scalable data pipelines that pull information from diverse sources into context/memory stores for AI agents
Design and implement event sourcing architecture with distributed systems to ensure data integrity and reliability
Create data infrastructure that feeds both AI context systems and analytics data warehouses
Partner with applied AI engineers to develop optimal context systems for agent performance
Participate in AI agent development and prompt engineering to better understand context needs
Collaborate with the CTO on architectural decisions for data and AI systems
Partner with growth, sales, and data science teams to define the right data events and metrics for capturing high-quality data
Develop integrations with PostHog, ClickHouse, Turntable, and potentially Snowflake
Implement vector databases like Qdrant for efficient AI context retrieval
Design data schemas and models that optimize for both AI agent context and analytics use cases

You're Our Person If

You're passionate about building data infrastructure that powers AI systems
You have deep expertise with distributed systems, event sourcing, and data pipeline architecture
You have some experience with or interest in AI agent development and prompt engineering
You understand CAP theorem tradeoffs and can make appropriate architectural decisions for data systems
You have experience with ELT/ETL tools like Apache Airflow, Temporal, Airbyte, or N8N
You're experienced with vector databases and embedding models for AI context
You have worked with analytics warehouses like ClickHouse, Snowflake, or BigQuery
You can balance technical excellence with pragmatic solutions that deliver business value
You collaborate effectively with applied AI engineers and understand their context needs
You ship features daily and take immediate action instead of overthinking
You embrace "there is no try, there is just do" as your engineering mantra

Hard Requirements

Strong experience with Python and data engineering frameworks
Deep understanding of distributed systems principles, CAP theorem tradeoffs, and event sourcing architecture
Experience designing and implementing data pipelines using tools like Apache Airflow, Temporal, Airbyte, or N8N
Proven track record building production data systems that power AI/ML applications
Experience with vector databases (like Qdrant, Pinecone, or Weaviate)
Basic understanding of AI agent development and prompt engineering concepts
Familiarity with analytics tools like PostHog, ClickHouse, and data warehousing solutions
Knowledge of data modeling and schema design for both operational and analytical purposes
Strong problem-solving skills and ability to work in a fast-paced startup environment
Previous experience at an early-stage startup preferred
Must be based in San Francisco and work in-office 5.5 days per week (relocation assistance provided)

Our Tech Stack

We're building a modern, AI-native data infrastructure to power our growth:

Data & AI Infrastructure:

Event sourcing architecture with distributed systems design for reliability
Apache Airflow, Temporal, Airbyte, and N8N for data pipeline orchestration
Qdrant and other vector databases for AI context storage and retrieval
PostHog for product analytics and event tracking
ClickHouse for high-performance analytics queries
Turntable for data visualization and dashboarding
Potential Snowflake integration for enterprise data warehousing
Redis streams and PostgreSQL for operational data storage
Logfire for comprehensive observability and analytics

AI Integration:

Context-aware AI agents powered by your data pipelines
Claude (Anthropic), GPT-4.1 (OpenAI), and select open source models
RAG systems utilizing the context data you provide
Custom embedding models optimized for our insurance domain

What You'll Build in Your First 90 Days

First Month:

Set up core data infrastructure and implement event sourcing architecture
Build initial data pipelines connecting our primary data sources to AI context stores
Implement basic vector storage for AI agent context
Design schema and event tracking plan with the growth and data science teams
Establish data quality monitoring and alerting
Shadow applied AI engineers to understand their context needs for agents

Second Month:

Expand data pipeline coverage to include additional sources and destinations
Implement more sophisticated context retrieval systems for AI agents
Build integrations with PostHog and ClickHouse for analytics use cases
Work with applied AI engineers to optimize context for specific agent tasks
Participate in basic prompt engineering to better understand context requirements
Create automated testing for data pipelines to ensure reliability

Third Month:

Optimize performance and reliability of data pipelines
Implement advanced context features like temporal awareness and entity relationships
Build dashboards and reporting tools for monitoring AI context quality
Assist in development of simple AI agents leveraging your context systems
Collaborate with applied AI engineers to tune context systems for agent performance
Scale the data infrastructure to handle increasing volumes and velocity

Our Data Philosophy

Context is King: The quality of AI decisions directly correlates with the quality of context available
Event-Driven Architecture: Design systems that capture, process, and react to events for maximum data fidelity
Single Source of Truth: Maintain consistency across operational and analytical data systems
Data-Informed Growth: Enable growth and sales teams with the right metrics and insights
CAP Theorem Understanding: Make intelligent tradeoffs between consistency, availability, and partition tolerance
Lambda Architecture Approach: Combine event streaming for real-time processing with batch processing for complete analytics
Action Orientation: Always default to action - ship code, gather data, and iterate rather than overthink or overplan
Execution Focus: There is no try, there is just do - we value engineers who build and ship, not just plan and strategize
Strong Opinions: Form and express clear viewpoints that can be tested against reality to generate valuable information
Observable & Accountable: Ensure comprehensive monitoring of all data systems and pipelines

Join Us To Transform the $900B Insurance Market

This is an early-stage role at a fast-moving startup, and you'll often experience the crawl-walk-run approach to building. You'll quickly prototype data pipelines and then push them into productionized systems that can scale. We're looking for people who can be creative in providing impact first, then take learnings from that impact and push them back into the system.

You should ideally have worked in an early-stage startup environment and understand the pacing. This is a fast-paced environment where we value ownership and quick, rapid feedback loops within the team. You'll work directly with the CTO, our applied AI engineers, the CEO, growth team, and sales team to execute on our vision with a bias toward action.

We require you to be in San Francisco and work from our office 5.5 days per week. We'll cover relocation costs and believe the best teams collaborate intensively in person.

Skills

Python, Event Sourcing, Distributed Systems, CAP Theorem, Lambda Architecture, Data Engineering, Apache Airflow, Temporal, Airbyte, N8N, Qdrant, Vector Databases, PostHog, ClickHouse, Turntable, Snowflake, ETL/ELT, Data Modeling, Event-Driven Architecture, AI Agent Development, Prompt Engineering