Data engineer in knowledge graphs

Most generative AI deployments fall apart in sensitive environments because they lack sovereign data foundations. Backed by over $70M from top-tier global venture firms, this team builds secure, air-gapped AI infrastructure for defence, national security, banking, and critical infrastructure. Led by veterans of elite enterprise data platforms and defence engineering, they are solving how modern models safely reason over classified and regulated information. This is not a routine analytics ETL role building dashboards for business operations. You will design the core semantic data foundations: turning messy APIs, document stores, and event streams into unified knowledge graphs and queryable data products that autonomous AI agents rely on to reason.

• Design and evolve enterprise ontologies alongside domain experts, defining stable identifiers, relationship semantics, and schema versioning.
• Build ingestion and transformation pipelines that consolidate APIs, databases, unstructured files, and event streams into coherent graph, relational, and NoSQL data layers.
• Implement robust entity resolution, deduplication, and source reconciliation systems that keep data lineage and classification tags intact across every hop.
• Expose performant query interfaces and data retrieval services to downstream AI assistants and application services, enforcing automated semantic constraints.
• Own schema migrations, automated model validation, and deployment recovery strategies across cloud and air-gapped environments alongside DataOps.

• Production mastery of Python and SQL, with a track record of building well-tested pipelines, complex relational models, and query-optimised schemas.
• Hands-on experience operating a graph database or triple store in production (such as Neo4j/Cypher or RDF/SPARQL), with a strong grasp of the architectural trade-offs between property graphs and RDF.
• Proven track record designing real-world ontologies from scratch, including stable identifiers, relationship semantics, versioning, and entity resolution across heterogeneous sources.
• Demonstrated ability to turn ambiguous domain data into reliable, documented interfaces within a high-growth startup where requirements are not handed to you on a spec sheet.
• Based in or willing to work out of London.

• Условий в вакансии нет