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.
