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.
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
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’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 StackBackend: 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 NationGraphOwn 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.
Government contains enormous amounts of valuable information that is technically public but practically inaccessible.
Your job is to change that.
Work with exceptional people
You’ll work closely with the CEO, CTO, and a small engineering and research team.
The team has backgrounds spanning high-scale infrastructure, quantitative finance, AI, and startups.
Have real ownership
We move quickly.
We operate with very little bureaucracy.
Engineers have significant ownership over technical decisions and product outcomes.
If the idea of building the data infrastructure to map and understand how government works sounds exciting, we’d love to talk.
Skills Required
- Strong engineering ability with genuine interest in data
- Experience owning significant production data systems end-to-end
- Strong proficiency in Python, Go, or another systems/backend language
- Highly proficient with SQL
- Understanding of distributed data systems, including orchestration, idempotency, backfills, retries, observability, lineage, and failure recovery
- Experience with large-scale external data, crawling, information retrieval, entity resolution, knowledge graphs, or document processing
- Interest in using LLMs and modern machine learning in data infrastructure
- Strong commitment to data quality, correctness, and reliability
- Strong product judgment regarding the value of acquiring data
- Ability to thrive in ambiguity and create architecture independently
What We Do
NationGraph is transforming public sector sales by unlocking a single source of truth for every government purchase decision. We empower sales teams with real-time insights—covering purchase orders, meeting minutes, procurement rules, and more—so they can bypass endless spreadsheets and focus on building genuine relationships. We provide: Actionable Data: Access organized insights into purchase orders, decision-maker signals, and contract timelines in one platform. Proactive Workflows: Get automated alerts when key events, like RFP releases or upcoming renewals, occur—cutting weeks off your prospecting cycle. Deep Context: Leverage parsed meeting minutes and regulatory insights to understand the “who, what, when, where, and why” behind every transaction. NationGraph is on a mission to make public sector sales more transparent and efficient. We’re here to decode the chaos, empowering you to seize opportunities that others miss. We are backed by public-sector investing veterans like XYZ Venture Capital who have backed industry leaders such as Anduril, Apex Space, and Verkada—along with Reach Capital, Go Global Ventures, and angel investors who have founded and build iconic companies like OpenGov and Clever. Join us as transform how government data is accessed and used and help reshape how the public sector connects with the vendors Learn more or schedule a demo at NationGraph.com We're also hiring exceptional talent in engineering, data science, AI & ML, and go-to-market.
