Senior Knowledge Graph Engineer

Role Overview We 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 Profile The 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.