Staff Engineer, Data Platform

About this role

STAFF ENGINEER, DATA PLATFORM

ABOUT NATIONGRAPH

NationGraph is building the data and intelligence layer for the public sector.

- More than 110,000 state and local government agencies across the U.S. independently publish information about:

- How they operate

- What they buy

- Who they work with

- What problems they are trying to solve

- That information is fragmented across millions of websites, documents, databases, procurement systems, meeting records, and public records.

- NationGraph turns that information into structured, connected, actionable intelligence for businesses selling to government.

- Founded in 2024, NationGraph is dedicated to making uncommon knowledge common, because public data should actually be public.

THE ROLE

We’re looking for a Staff Engineer, Data Platform to own one of the most important technical problems at NationGraph: turning the outside world’s fragmented government information into a proprietary data advantage.

This is not a traditional data engineering role focused on maintaining a warehouse or internal analytics.

You’ll own the technical ecosystem that:

- Discovers external data

- Acquires it reliably

- Understands and extracts information from it

- Normalizes and connects it

- Validates its quality

- Makes it available to NationGraph’s products and models

The scope starts with more than 110,000 independent state and local government agencies, but extends to federal data, Canada, and eventually public-sector information globally.

You’ll work across:

- Data engineering

- Distributed systems

- Information retrieval

- Data modeling

- LLMs and agents

- Applied ML

- Entity resolution

- Knowledge graphs

You’ll partner closely with Product, ML Research, and Infrastructure to determine both:

- How we acquire data

- What data NationGraph should have that nobody else does

WHAT YOU’LL DO

- Own our external data platform end-to-end

- Design systems spanning discovery, acquisition, extraction, normalization, entity resolution, validation, storage, serving, and monitoring.

- Establish the architecture and abstractions other engineers build on.

- Map the world of government data

- Develop a deep understanding of where government information lives.

- Understand how it is published, how it changes, and how information across thousands of institutions can be connected.

- Build systems for messy, real-world data

- Work across government websites, APIs, procurement systems, PDFs, spreadsheets, meeting records, and public records.

- Build for changing schemas, broken sources, conflicting records, and edge cases.

- Use AI to rethink the traditional data stack

- Work with our ML Research team to use LLMs, agents, and emerging models to:

- Discover new sources

- Understand unfamiliar schemas

- Extract structured information

- Resolve entities

- Monitor data quality

- Detect when sources change

- Build proprietary data flywheels

- Create systems where more data improves our models.

- Use better models to discover and understand more data.

- Continuously expand NationGraph’s underlying knowledge graph.

- Set technical direction

- Define the architecture for how NationGraph acquires and represents public-sector information.

- Make decisions that will shape the platform over the next several years.

- Help determine which technical investments create the strongest long-term data advantage.

YOU MIGHT BE A GOOD FIT IF

- You’re an unusually strong engineer who genuinely enjoys working with data.

- You’ve owned significant production data systems end-to-end.

- You enjoy the detective work of making sense of unfamiliar, messy datasets.

- You’re strong in Python, Go, or another systems/backend language.

- You’re highly proficient with SQL.

- You understand distributed data systems, including:

- Orchestration

- Idempotency

- Backfills

- Retries

- Observability

- Lineage

- Failure recovery

- You have experience with one or more of:

- Large-scale external data

- Crawling

- Information retrieval

- Entity resolution

- Knowledge graphs

- Document processing

- You’re excited about using LLMs and modern ML as components of data infrastructure.

- You care deeply about data quality, correctness, and reliability.

- You have strong product judgment and can reason about what data is actually worth acquiring, not just how to acquire it.

- You thrive in ambiguity and would rather create the architecture than be handed one.

We’re particularly interested in backgrounds spanning:

- Alternative data

- Quantitative research infrastructure

- Search and crawling

- AI data infrastructure

- Knowledge graphs

- Large-scale document processing

- Data aggregation

None of these are requirements.

OUR ENGINEERING STACK

- Backend: Python, Go, PostgreSQL

- Infrastructure: Redis, Docker, Kubernetes

- Frontend: React, TypeScript

- AI / ML: LLMs, agents, proprietary models, and emerging frontier-model research

Our stack will evolve. At Staff level, you’ll help decide how.

WHY NATIONGRAPH

- Own a foundational problem

- A large part of this architecture still needs to be invented.

- You’ll have significant ownership over how NationGraph discovers, acquires, represents, and serves public-sector information.

- Work on a genuinely hard data problem

- There is no single API for American government.

- There are tens of thousands of institutions, millions of sources, inconsistent schemas, and enormous amounts of information buried in systems never designed for machines.

- Build a real data moat

- We believe a major long-term advantage in applied AI will come from proprietary context and data.

-…