Knowledge Graph Expert

Role Summary

We are seeking an experienced Knowledge Graph Expert to join our Data & AI practice, specializing in the design, development, and operationalization of enterprise knowledge graphs on Microsoft Fabric and Azure. The ideal candidate will bridge semantic data modeling, graph-based AI reasoning, and enterprise data governance to deliver intelligent, connected data assets across different business and manufacturing functions.

This role requires deep hands-on expertise in Microsoft Fabric's OneLake and GraphQL layers, Azure AI Search, and graph database technologies, combined with the ability to translate complex business problems into knowledge graph architectures that power agentic AI, data governance, and analytics solutions.

Key Responsibilities

Knowledge Graph Architecture & Design

• Design and implement enterprise-grade knowledge graphs on Microsoft Fabric, leveraging OneLake, Direct Lake mode, and semantic model layers.
• Develop ontologies, taxonomies, and entity-relationship models aligned to business domains (financial services, healthcare, aviation).
• Architect graph-based data products integrating structured (SQL, lakehouse), semi-structured (JSON-LD, RDF), and unstructured sources.
• Define standards for graph schema governance, versioning, and lineage tracking.

Microsoft Fabric & Azure Integration

• Build knowledge graph pipelines using Microsoft Fabric Data Engineering (Spark, notebooks, lakehouses) and Data Factory.
• Integrate Azure AI Search knowledge store with graph indexes for semantic retrieval and GraphRAG patterns.
• Connect graph models to Power BI semantic models via Direct Lake for real-time, governed analytics.
• Leverage Azure OpenAI and Fabric Copilot capabilities for natural language querying over knowledge graphs.
• Implement Fabric Real-Time Intelligence (Eventstream, KQL) for dynamic knowledge graph updates.

Graph AI & Agentic Systems

• Design GraphRAG (Graph Retrieval-Augmented Generation) pipelines that ground LLMs with structured enterprise knowledge.
• Build agentic AI workflows using graph traversal for context-aware reasoning (e.g., Azure AI Foundry, AutoGen, Semantic Kernel, MCP-enabled agents).
• Develop entity resolution and disambiguation pipelines using NLP and graph embeddings.
• Design and expose knowledge graph capabilities as MCP (Model Context Protocol) servers, enabling LLM agents to query, traverse, and update graph entities through standardized tool interfaces.
• Implement graph-based fraud detection, risk scoring, and compliance reasoning patterns for financial clients.

Data Governance & Metadata Management

• Align knowledge graph assets with enterprise data governance frameworks (Informatica EDC/Axon, Microsoft Purview).
• Model regulatory and policy knowledge graphs for GCC compliance frameworks (PDPPL, QCB, CBUAE, NPC).
• Publish and steward graph-based business glossaries and data lineage maps integrated with data catalogs.
• Enforce data quality rules and semantic constraints within graph structures.

Client Delivery & Stakeholder Engagement

• Lead client workshops to elicit domain knowledge and translate it into graph ontologies and use cases.
• Produce architecture blueprints, technical proposals, and proof-of-concept demonstrations for pre-sales engagements.
• Mentor junior data engineers and solution architects on graph modeling best practices.
• Collaborate with data scientists, AI engineers, and enterprise architects on cross-functional AI platforms.

Required Skills & Qualifications

Core Graph Technologies

• Cypher (Neo4j), Proficiency in graph query languages:
• SPARQL (RDF/OWL triple stores — Apache Jena, AWS Neptune, Stardog)
• Gremlin (Azure Cosmos DB Graph API, TinkerPop)
• OpenCypher (Memgraph, Amazon Neptune Analytics)
• Hands-on ontology engineering using OWL, RDFS, SHACL, and SKOS standards.
• Experience with property graph and RDF graph modeling paradigms and trade-off analysis.

Microsoft Fabric (Mandatory)

• Demonstrated experience with Microsoft Fabric end-to-end: OneLake, Lakehouses, Warehouses, Data Pipelines, Notebooks.
• Familiarity with Fabric GraphQL API for exposing knowledge graph entities as managed data products.
• Building semantic models in Fabric for Power BI Direct Lake and AI-ready data assets.
• Integration with Microsoft Purview for unified data governance across Fabric workspaces.
• Experience with Fabric Copilot, AI Skills, and Real-Time Intelligence workloads.

Azure AI & Cloud Platform

• Azure AI Search — knowledge store, indexers, semantic ranking, vector hybrid search.
• Azure OpenAI Service — prompt engineering, GraphRAG, function calling for graph traversal.
• Azure Databricks or Fabric Spark for large-scale graph processing (GraphX, GraphFrames).
• Azure Data Factory / Fabric Data Factory for ETL pipelines feeding knowledge graphs.
• Azure Cosmos DB (Gremlin API) for operational graph workloads.

Data Engineering & Programming

• Python (RDFLib, NetworkX, PyKEEN, SPARQLWrapper, LangChain graph tools).
• PySpark for distributed graph data processing on Fabric / Databricks.
• Experience with NLP pipelines for entity extraction and relationship mining (spaCy, Azure Language Service, GLiNER).
• Knowledge of vector databases (Azure AI Search, Qdrant, Weaviate) for embedding-based graph enrichment.
• Familiarity with Model Context Protocol (MCP) for building graph-backed tool servers that expose SPARQL/Gremlin query capabilities, ontology lookups, and entity resolution as callable tools for LLM agents (Claude, GPT-4, Azure AI Foundry).

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related field.
• 7+ years of experience in data architecture or data engineering, with 3+ years focused on knowledge graphs.
• Microsoft certifications preferred: DP-600 (Fabric Analytics Engineer), DP-900, AI-102.
• Experience in GCC regulated industries (banking, healthcare, government) strongly preferred.