Semantic Graph & Ontology Architect

We’re building a Smart Data Fabric that unifies enterprise data (Snowflake, SharePoint, ERP, NoSQL, and document silos) and exposes it to advanced AI agents through a semantic, graph-native, and vector-aware foundation. You will own graph and semantic architecture, modeling business relationships, processes, and logic to enable accurate, contextual, auditable workflows. This is a hands-on leadership role spanning LPG vs RDF/OWL tradeoffs, query optimization, and ontology engineering., Graph & Semantic Architecture

• Design scalable graph schemas (LPG and/or RDF/OWL) based on semantic and inference requirements.

• Author and optimize Cypher/Gremlin/SPARQL queries for multi-hop traversal, orchestration, and complex reasoning.

• Define canonical entity models and mapping layers across Snowflake, MongoDB, SharePoint, ERP, and unstructured content.

Ontology Engineering & Reasoning

• Create and maintain formal ontologies/taxonomies; govern versioning and lifecycle.

• Implement logical inference (rules/constraints) for agent decision-making, conflict detection, and workflow integrity.

• Establish semantic consistency standards and data quality checks.

Hybrid Semantic Layer (Graph + Logic)

• Design a hybrid semantic layer combining graph context with business logic and access controls for semantic search, multi-hop traversal, and knowledge contextualization.

• Model RACI/RBAC as graph edges/nodes; embed compliance rules and auditability.

APIs, Patterns & Collaboration

• Define clean API layers for semantic enrichment and retrieval; deliver reference implementations and patterns.

• Specify MCP-based (Model Context Protocol) tool discovery/invocation patterns; collaborate with platform engineers for agent connectivity.

• Partner with data/platform/security teams on ingestion pipelines, lineage, governance, and observability requirements.

Quality, Performance & Governance

• Set query performance budgets; prevent Cartesian explosions; ensure index utilization.

• Establish lineage and data governance artifacts (semantic catalogs, policy nodes, audit trails).

• Document standards and mentor engineers adopting graph/semantic patterns.

• Bachelor’s/Master’s in CS, Data Science, Mathematics, Engineering, or related field.

• 7-12 years in graph databases, semantic modelling, ontology engineering.

• Deep expertise in Cypher, Gremlin, SPARQL; strong command of LPG vs RDF/OWL tradeoffs.

• Hands-on with Neo4j, AWS Neptune, TigerGraph, Stardog (at least one in production).

• Experience mapping enterprise data (Snowflake/MongoDB/SharePoint/ERP) into graph/ontology layers.

• Strong understanding of RBAC/RACI, data governance, lineage, and security controls.

• Ability to design clean APIs and reference implementations for semantic enrichment/retrieval.

• Practical AWS familiarity (IAM, VPC, S3, EKS/ECS/Lambda) in collaboration with platform teams.

Preferred Qualifications

• Ontology tooling (Protégé, SHACL/SWRL), reasoning engines, and constraint modeling.

• Prior delivery of enterprise knowledge graphs supporting workflows & audit trails.

• Exposure to vector retrieval/RAG and how graph context informs re-ranking.

• Observability awareness (tracing across graph layers, OpenTelemetry, Prometheus/Grafana).

• Experience with Snowflake/MongoDB/SharePoint APIs and ERP data structures

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