Title : Knowledge Graph Engineer Position Summary 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. - Solid 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. - 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.

📌 Knowledge Graph Engineer (Pune)
🏢 Siemens
📍 Pune