Senior Knowledge Graph Engineer
Role OverviewWe are seeking a senior hands-on engineer to design, build, and productionize knowledge graph and semantic data systems for enterprise AI use cases in a regulated financial services environment.This role will focus on graph databases, ontology implementation, entity resolution, relationship modeling, provenance, and integration of structured and unstructured data into graph-enabled intelligence workflows. The core need is a senior engineer who can assess technical tradeoBs, build durable systems, and turn early graph / ontology concepts into working infrastructure.Core Responsibilities Design and build knowledge graph systems supporting company, issuer, investor, transaction, document, and market-intelligence use cases. Implement graph data models, ontology structures, entity types, canonical identifiers, relationship predicates, provenance, and temporal attributes. Develop entity resolution, deduplication, canonicalization, and relationshipnormalization pipelines across structured and unstructured data sources. Build ingestion and transformation workflows that convert documents, source data, and extracted facts into graph-ready representations. Evaluate and implement graph database technologies, including tradeoBs across property graph, RDF / OWL, relational, and hybrid approaches. Integrate graph systems with internal data platforms, APIs, AI extraction / validation workflows, and downstream application surfaces. Define validation, confidence scoring, quarantine, and human-review workflows for graph assertions. Create technical design documents, data model specifications, implementation plans, and operational documentation. Partner with applied AI, data, product, engineering, and business stakeholders to move graph-enabled capabilities from prototype to production.Critical Skills Deep hands-on experience with knowledge graphs, graph databases, ontology implementation, semantic data modeling, and entity resolution. Strong engineering experience with data pipelines, APIs, backend systems, and production integration patterns. Practical experience with graph technologies such as Neo4j, Amazon Neptune, TigerGraph, Stardog, RDF / OWL, SPARQL, Cypher, or similar. Strong understanding of canonical IDs, entity matching, relationship modeling, provenance, temporal data, data lineage, and graph quality controls. Familiarity with LLM-based extraction, classification, normalization, retrieval, or validation workflows. Strong Python, data engineering, and backend development skills. Ability to operate independently in ambiguity and produce clear technical documentation.Preferred Background Experience building graph or semantic data systems for financial services, market intelligence, enterprise search, compliance, risk, research, or other complex entity / relationship domains. Experience integrating structured data, documents, web content, and third-party data into graph-based systems. Experience with human-in-the-loop validation, data quality workflows, source attribution, and auditability. Experience working with cloud data environments, enterprise data platforms, and AI enabled analytics systems.Success ProfileThe ideal candidate can take a complex real-world domain, define the core entities and relationships, and engineer a graph-backed system that is accurate, traceable, maintainable, and useful. Success in this role means turning early graph / ontology concepts into production oriented infrastructure that supports entity resolution, relationship intelligence, source backed evidence, temporal facts, and AI-enabled enterprise intelligence workflows.