Knowledge Graph / Semantic Data Engineer

We are looking for an experienced engineer to help the client build AI-ready enterprise data foundations in the Retail and FinTech domain, where complex business processes, governance, and connected data are critical for enterprise AI adoption.
  
You'll be focused on turning fragmented enterprise data into structured, connected knowledge that AI systems can understand and reason over. You’ll design and implement enterprise knowledge graphs, semantic data products, and business semantic layers that enable AI, analytics, and intelligent automation.

Responsibilities
 

• Design and implement enterprise knowledge graphs.
• Build semantic layers that create a common business understanding across enterprise systems.
• Model business entities, relationships, taxonomies, and ontologies.
• Develop semantic data products that can be consumed by AI applications, analytics platforms, and business users.
• Design enterprise metadata models and integrate with data catalogues.
• Connect structured and unstructured enterprise data into a unified semantic model.
• Collaborate with data architects, domain experts, business stakeholders, and AI teams to capture business meaning.
• Support enterprise AI initiatives by creating machine-readable business context.
• Ensure governance, lineage, metadata quality, and semantic consistency across data assets.
• Integrate semantic models with modern data platforms and cloud ecosystems.

Key requirements
 

• Strong experience designing and implementing Knowledge Graphs.
• Experience with semantic technologies, including RDF, OWL, SKOS, SPARQL, or similar standards.
• Experience with graph databases such as Neo4j, Amazon Neptune, Stardog, GraphDB, or equivalent.
• Understanding of enterprise metadata management and data catalogues.
• Experience building semantic models, ontologies, taxonomies, and controlled vocabularies.
• Knowledge of enterprise data architecture and data modelling.
• Strong SQL skills and experience integrating data from multiple enterprise systems.
• Experience working with cloud data platforms (Azure, AWS, or GCP).
• Familiarity with AI, LLMs, RAG architectures, or enterprise AI solutions is a strong advantage.

Nice to Have

• Experience with semantic search or enterprise search.
• Experience with Master Data Management (MDM).
• Knowledge of Data Mesh or Data Products.
• Experience with enterprise governance frameworks.
• Experience supporting AI agent or GenAI implementations.