Job description
Title : Knowledge Graph Engineer Position Summary We are building a connected enterprise knowledge layer that unifies structured and unstructured data across business systems and enables intelligent search, contextual retrieval, semantic reasoning, and AI-driven workflows. In this role, you will design and implement scalable knowledge graph solutions that model business entities, relationships, and domain logic to support advanced analytics, semantic applications, and next-generation AI use cases. This is a hands-on engineering role spanning graph modeling, ontology development, semantic enrichment, and enterprise data integration. How You’ll Make an Impact (Responsibilities of Role) Knowledge Graph Design & Engineering • Design scalable knowledge graph schemas using property graph and/or RDF-based models. • Develop and optimize graph queries using Cypher, SPARQL, or Gremlin. • Model business entities, relationships, hierarchies, and context across domains. • Build ingestion pipelines to transform enterprise data into graph structures. Semantic Modeling & Ontology Development • Create and maintain ontologies, taxonomies, and semantic models. • Define canonical entity models and semantic mappings across data sources. • Establish semantic validation, consistency standards, and data quality checks. • Support ontology lifecycle management and schema evolution. Data Integration & Semantic Enrichment • Collaborate with engineering teams to ingest, transform, and enrich enterprise data. • Support entity resolution, metadata enrichment, and relationship extraction. • Enable semantic search, intelligent assistants, and knowledge-driven workflows. APIs, Collaboration & Platform Enablement • Design and support graph APIs and semantic access layers. • Partner with product, architecture, security, and domain teams on graph solutions. • Document graph modeling standards, patterns, and best practices. Quality, Governance & Performance • Optimize query performance, indexing, and traversal efficiency. • Contribute to metadata, lineage, governance, and access control practices. • Ensure graph solutions are scalable, secure, and aligned with enterprise data standards. What You Bring (Required Qualifications and Skill Sets) • Bachelor’s/master’s degree in computer science, Data Science, Engineering, Information Systems, Mathematics, or a related field. • 5–7 years of experience in knowledge graph engineering, graph databases, semantic modeling, ontology engineering, or related data architecture roles. • Strong hands-on experience with at least one graph platform such as Neo4j, AWS Neptune, Stardog, TigerGraph, GraphDB, or similar technologies. • Proficiency in graph query languages such as Cypher, SPARQL, or Gremlin. • Experience designing graph schemas, semantic data models, taxonomies, and ontology-aligned structures for enterprise use cases. • Good understanding of knowledge graphs, RDF, OWL, semantic web concepts, ontology design, and linked data principles. • Experience integrating enterprise data from sources such as relational databases, APIs, document repositories, cloud platforms, and business applications. • Strong skills in Python and SQL for data transformation, graph ingestion, enrichment, and query support. • Familiarity with data governance, metadata management, lineage, access control, and enterprise data quality practices. • Ability to work cross-functionally with engineers, architects, business stakeholders, and domain experts to translate business concepts into scalable graph models. Preferred Qualifications • Experience with ontology tools such as Protégé and semantic validation frameworks such as SHACL or similar approaches. • Exposure to inference, reasoning engines, rule-based modeling, or semantic constraint design. • Experience building enterprise knowledge graphs for semantic search, AI copilots, document intelligence, workflow automation, or recommendation engines. • Familiarity with vector search, RAG, hybrid graph + AI architectures, or semantic retrieval patterns. • Exposure to cloud environments such as AWS, Azure, or GCP in support of graph deployment and enterprise integration. • Understanding of observability, graph query tuning, and semantic layer performance monitoring is a plus.
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