Staff Engineer, Data Platform

Responsibilities

• Own the external data platform end-to-end.
• Design systems for data discovery, acquisition, extraction, normalization, entity resolution, validation, storage, serving, and monitoring.
• Establish the architecture and abstractions used by other engineers.
• Develop systems that handle changing schemas, broken sources, conflicting records, and other messy real-world data.
• Use LLMs, agents, and emerging models to discover sources, understand schemas, extract structured information, resolve entities, monitor quality, and detect source changes.
• Build proprietary data flywheels and continuously expand the underlying knowledge graph.
• Define technical direction for acquiring and representing public-sector information and make long-term platform investment decisions.
• Partner with Product, ML Research, and Infrastructure on data acquisition and platform strategy.

Requirements

• Be an unusually strong engineer who enjoys working with data and has owned significant production data systems end-to-end.
• Strong proficiency in Python, Go, or another systems/backend language and high proficiency with SQL.
• Understand distributed data systems, including orchestration, idempotency, backfills, retries, observability, lineage, and failure recovery.
• Have experience with one or more of large-scale external data, crawling, information retrieval, entity resolution, knowledge graphs, or document processing.
• Be interested in using LLMs and modern ML as components of data infrastructure.
• Care deeply about data quality, correctness, and reliability.
• Demonstrate strong product judgment about which data is worth acquiring.
• Thrive in ambiguity and be able to create architecture rather than receive a predefined design.
• Backgrounds in alternative data, quantitative research infrastructure, search and crawling, AI data infrastructure, knowledge graphs, large-scale document processing, or data aggregation are of interest but are not required.

Benefits

• Significant ownership over technical decisions, platform architecture, and product outcomes.
• Opportunity to solve a foundational, genuinely difficult public-sector data problem and build a proprietary data advantage.
• Work closely with the CEO, CTO, and a small engineering and research team with backgrounds in infrastructure, quantitative finance, AI, and startups.
• Fast-moving environment with little bureaucracy and substantial technical ownership.