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NationGraph is building the data and intelligence layer for the public sector, turning fragmented government information across 110,000+ state and local agencies into structured, actionable intelligence for businesses selling to government.
As Staff Engineer, Data Platform, you will own the technical ecosystem that discovers, acquires, understands, normalizes, validates, and serves external government data. This is not a traditional data warehouse role—you'll architect systems spanning discovery, acquisition, extraction, normalization, entity resolution, validation, storage, serving, and monitoring.
Key responsibilities:
- Own the external data platform end-to-end, establishing architecture and abstractions for other engineers
- Map the world of government data across websites, APIs, procurement systems, PDFs, spreadsheets, and public records
- Build systems for messy, real-world data with changing schemas, broken sources, and conflicting records
- Partner with ML Research and Infrastructure teams to use LLMs, agents, and emerging models for discovery, extraction, entity resolution, and quality monitoring
- Create proprietary data flywheels where better models discover and understand more data
- Set technical direction and make decisions shaping the platform's long-term data advantage
You should have:
- Proven track record owning significant production data systems end-to-end
- Strong proficiency in Python, Go, or similar systems/backend languages and SQL
- Deep understanding of distributed data systems (orchestration, idempotency, backfills, retries, observability, lineage, failure recovery)
- Experience with large-scale external data, crawling, information retrieval, entity resolution, knowledge graphs, or document processing
- Genuine enthusiasm for using LLMs and modern ML as data infrastructure components
- Strong product judgment about what data is worth acquiring
- Comfort thriving in ambiguity and creating architecture from first principles
Stack: Python, Go, PostgreSQL, Redis, Docker, Kubernetes, React, TypeScript, LLMs and proprietary models.
