Knowledge Graph Engineer | Semantic Data Specialist

🧠 We’re Hiring: Knowledge Graph Engineer | Semantic Data Specialist🕒 Employment Type: Full-Time💼 Level: Mid-Level to SeniorWe 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.🎯 Key Responsibilities

• 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, and semantic modeling practices.
• Participate in technical design reviews, project planning, testing, and continuous improvement activities.✅ Requirements
• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, Artificial Intelligence, Knowledge Engineering, or a related field.
• Proven experience in knowledge graph engineering, semantic data modeling, graph databases, or related disciplines.
• Strong understanding of graph theory, data modeling, entity relationships, and knowledge representation.
• Hands-on experience with graph database technologies such as Neo4j, Amazon Neptune, GraphDB, Stardog, or equivalent platforms.
• Proficiency in graph query languages such as Cypher, SPARQL, or Gremlin.
• Familiarity with semantic web standards, including RDF, RDFS, OWL, JSON-LD, and linked data principles.
• Experience designing ontologies, taxonomies, controlled vocabularies, or domain-specific semantic models.
• Strong understanding of relational databases, SQL, data structures, and data integration concepts.
• Experience building data ingestion, transformation, and enrichment pipelines.
• Proficiency in Python or another relevant programming language for data processing and automation.
• Knowledge of REST APIs, data serialization formats, and application integration techniques.
• Experience with entity resolution, record linkage, relationship extraction, or knowledge graph population is advantageous.
• Familiarity with natural language processing and information extraction techniques is beneficial.
• Understanding of knowledge graph applications in search, analytics, recommendation systems, and AI is preferred.
• Experience integrating knowledge graphs with large language models or RAG architectures is advantageous.
• Knowledge of cloud platforms and graph services is beneficial.
• Understanding of data governance, metadata management, data quality, and information architecture principles.
• Strong analytical and problem-solving skills.
• Ability to translate complex business concepts into structured semantic models.
• Excellent attention to detail and commitment to data accuracy and consistency.
• Strong technical documentation and communication skills.
• Ability to collaborate with engineers, architects, analysts, researchers, and business stakeholders.
• Ability to manage multiple technical priorities and deliver projects within agreed timelines.
• Familiarity with Git, automated testing, CI/CD, and software development best practices is advantageous.
• Relevant certifications or specialized training in graph databases, semantic technologies, data engineering, or knowledge representation are beneficial.🌟 What We Offer
• Competitive salary and comprehensive employee benefits.
• Opportunity to work with advanced knowledge graph and semantic data technologies.
• Exposure to AI, knowledge representation, graph analytics, and intelligent information systems.
• Professional development in data engineering, semantic modeling, and graph architecture.
• Training and support for relevant technical certifications.
• Opportunities to work on enterprise data integration and knowledge management projects.
• Collaboration with experienced data engineers, architects, AI specialists, and domain experts.
• Opportunities to contribute to semantic search, AI-powered applications, and knowledge discovery initiatives.
• Access to modern graph databases, cloud platforms, and data engineering tools.
• Recognition for innovative solutions and improvements in data interoperability and knowledge discovery.
• Career progression toward Senior Knowledge Graph Engineer, Semantic Data Architect, Knowledge Engineering Lead, Graph Data Architect, or Head of Knowledge Engineering.