Graph Engineer | Semantic Data Specialist

Employment Type: Full-Time

Level: Mid-Level to Senior

We are seeking a technically skilled and analytical Knowledge Graph Engineer | Semantic Data Specialist to design, develop, and maintain knowledge graphs, semantic data models, ontologies, and intelligent data integration solutions. This role combines knowledge graph engineering, semantic technologies, data architecture, and information modeling to connect complex datasets, improve data discoverability, and enable advanced analytics, knowledge representation, and AI-driven applications.

Design, develop, and maintain enterprise knowledge graphs and semantic data solutions.

Build graph-based data models that represent entities, relationships, concepts, and business rules.

Develop and maintain ontologies, taxonomies, vocabularies, and semantic schemas.

Translate business requirements into structured knowledge representations and reusable data models.

Integrate data from relational databases, document repositories, APIs, data warehouses, and external sources.

Design entity relationships and graph structures that support efficient querying and knowledge discovery.

Develop data ingestion, transformation, normalization, and enrichment pipelines for knowledge graph platforms.

Implement entity resolution, entity linking, deduplication, and relationship extraction processes.

Map heterogeneous datasets to shared semantic models and standardized vocabularies.

Develop and optimize graph queries using technologies such as SPARQL, Cypher, or equivalent query languages.

Work with RDF, RDF Schema, OWL, JSON-LD, and other semantic web standards where applicable.

Build and maintain knowledge graphs using platforms such as Neo4j, GraphDB, Stardog, Amazon Neptune, or equivalent technologies.

Establish semantic relationships across business data, technical metadata, documents, and domain-specific knowledge.

Improve data interoperability by establishing consistent definitions, identifiers, schemas, and relationship models.

Collaborate with data engineers, data architects, data scientists, and domain experts to define semantic requirements.

Develop data validation rules and quality checks to maintain graph accuracy, consistency, completeness, and integrity.

Investigate inconsistencies, missing relationships, duplicate entities, and modeling issues within graph datasets.

Optimize graph storage, indexing, query performance, and processing efficiency.

Support the development of semantic search, recommendation systems, question-answering applications, and knowledge discovery tools.

Integrate knowledge graphs with machine learning models, natural language processing pipelines, and AI applications.

Support retrieval-augmented generation (RAG) solutions by connecting structured knowledge with large language models where appropriate.

Develop APIs and services that expose graph data and semantic relationships to downstream applications.

Maintain documentation for ontologies, data mappings, graph schemas, business definitions, and modeling decisions.

Establish reusable semantic modeling standards and best practices across projects.

Support knowledge graph deployment, monitoring, maintenance, and version control.

Conduct technical evaluations of graph databases, semantic technologies, and knowledge representation frameworks.

Perform impact analysis when changes to ontologies, schemas, or source data affect downstream systems.

Support data governance, metadata management, lineage, and enterprise information architecture initiatives.

Research emerging technologies in graph analytics, semantic AI, linked data, and knowledge representation.

Recommend improvements to graph architecture, data integration workflows,…