Develop agent infrastructure
Build retrieval, planning, and execution systems on top of real data
Support multi-step reasoning and analysis workflows
Integrate into internal tools
Build an end-to-end analytics system
Create a natural language interface over our entire data stack
Enable anyone to run complex analyses without writing SQL
Own and improve the data layer
Collaborate with product engineering to design and evolve clean, consistent data models
Identify inconsistencies in the core schema and resolve them at the source
Build and maintain data pipelines
Own the reliability of our data infrastructure end-to-end
Debug and fix broken systems (e.g. dbt models, pipeline failures)
Make it easy to integrate new data sources into the data warehouse
Make it trustworthy
Ensure outputs are correct, consistent, and explainable
Build evaluation loops, monitoring, and guardrails
Eliminate ambiguity in metrics, definitions, and sources of truth
Handle permissions, sensitive data, and edge cases
Drive adoption
Work directly with teams across EPD, Member Operations, Legal, and more
Drive alignment on data definitions and system usage
Turn this into the default way people interact with data

You are a product engineer working in data
You treat data models as part of the product, not a separate layer
You have strong opinions on schema design, naming, and consistency
You're comfortable identifying issues in production data and fixing them at the root
You are an owner, not a participant
You take vague, high-stakes problems and turn them into real systems
You care about outcomes, not just implementation
You have strong data experience
Experience with data modeling, warehouses, and pipelines
You're an exceptional builder
You can go from idea → architecture → production
You've built with LLMs, RAG, or agents in production
You've shipped systems that people actually rely on
You have influence
You communicate effectively with both technical and non-technical stakeholders
You can drive alignment across teams with competing priorities
You're comfortable challenging existing systems and pushing for better approaches.

Hybrid Work Model: Based in San Francisco with a balance of in-office and remote flexibility.
Fresh Lunch: Provided on in-office days.
Commuter Support: $150 monthly reimbursement for transit expenses.
Health & Wellness: $200 quarterly reimbursement to support your well-being.
Time Off: Flexible PTO plus 14 company holidays.
Comprehensive Coverage: 100% medical, dental, and vision for employees; 75% coverage for dependents.
Parental Leave: 16 weeks fully paid.
Retirement & Ownership: 401k plan plus an equity package.
Team Connection: Quarterly virtual events and an annual in-person summit.

AI Data Engineer – Agent Platform

Key skills