Semantic Layer - Ontology/Knowledge Engineer

JPMorgan Chase & Co.•Jersey City, NJ

About The Position

As an Ontology and Knowledge Graph Engineer in Chase's Data and Analytics Office, you will curate the semantic data assets that connect our enterprise data estate to a shared, intelligent knowledge graph. You will work at the intersection of formal knowledge representation, logical data modeling, and data integration, building the ontologies and mapping assets that make our data semantically interoperable across use cases. Our team values precision, intellectual curiosity, and a deep commitment to making data meaningful — and you will find that culture reflected in everything we build together. This role offers an opportunity to contribute to a foundational capability that underpins enterprise AI, analytics, and data governance at one of the world's most influential financial institutions.

Requirements

3 years of experience working with semantic web technologies, knowledge graph engineering, ontology development, or linked data systems in a professional or research setting
Demonstrated understanding of formal knowledge representation principles, including class hierarchies, property definitions, and logical constraints
Familiarity with data mapping concepts that connect structured and semi-structured data sources to ontology-defined target vocabularies
Working knowledge of semantic data model layers, including foundational data models, schema definition languages, and controlled vocabulary organization standards
Exposure to relational databases and semi-structured data sources, including REST APIs, in-memory structures, and streaming data pipelines
Ability to translate business and data requirements into formal semantic models in collaboration with data architects, domain experts, and engineering teams
Awareness of Virtual Knowledge Graph concepts and the principles of connecting heterogeneous data sources to a shared semantic layer without physical data movement.

Nice To Haves

Hands-on experience authoring ontologies and knowledge graph mapping assets in a production enterprise environment
Experience contributing to enterprise-scale knowledge graph programs within a large, complex organization
Familiarity with Reasoning and Semantic Validation frameworks used to enforce syntactic and semantic correctness of ontology-defined concepts
Exposure to Upper Ontology design patterns and their role in standardizing conceptual overlaps across multiple enterprise use cases
Experience communicating complex semantic modeling decisions to non-technical stakeholders, including business analysts and product owners
Familiarity with real-time data streaming platforms and their integration into knowledge graph mapping pipelines
Experience contributing to ontology governance programs, including versioning strategies and cross-domain alignment reviews

Responsibilities

Author Logical Data Model Ontologies that compose concepts from Upper Ontologies and Semantic Taxonomies to accurately represent how enterprise data is materialized across our data estate
Design and maintain Knowledge Graph Mapping assets that connect relational databases, REST APIs, in-memory data structures, and real-time streaming sources to a coherent enterprise knowledge graph
Curate Semantic Taxonomy structures using controlled vocabularies and Concept Schemes to organize enterprise concepts consistently across multiple business domains
Contribute to the design and governance of Upper Ontologies and Semantic Taxonomies that provide a shared, standardized conceptual backbone across enterprise semantic use cases
Enable Virtual Knowledge Graph capabilities by ensuring mapping assets and ontology definitions support on-the-fly knowledge graph materialization without physical data movement
Engage with data architects, domain subject matter experts, AI engineers, and machine learning engineers to align ontology and mapping design decisions with both physical data structures and downstream Reasoning and Semantic Validation requirements
Participate in ontology governance activities including versioning, change management, deprecation policies, and cross-domain alignment reviews
Translate complex business and data requirements into formal semantic representations that are technically rigorous and accessible to non-technical stakeholders.