With over 20 years of experience as an Enterprise Data & AI Architect, I specialise in graph-powered data solutions at enterprise scale — combining Neo4j with Azure Databricks and Unity Catalog to deliver knowledge graphs, data lineage platforms, and connected analytics across complex, multi-domain environments.

My Neo4j and Databricks work spans building graph-native pipelines on Delta Lake, orchestrating property graph models alongside medallion-architecture data flows, and exposing connected insights through semantic and analytics layers — enabling organisations to traverse relationships across millions of entities in real time and surface actionable intelligence at speed.

I design and deliver end-to-end AI and analytics platforms — from feature engineering and MLOps pipelines through to Agentic AI systems and LLM-powered data discovery — bridging raw enterprise data with production-grade machine learning and intelligent automation. Graph-native approaches are central to how I model context, lineage, and relationships that traditional analytics architectures cannot capture.

Industry experience spans upstream oil and gas, construction, and energy markets — including well data management, OSDU platform implementation, and subsurface data integration at major operators. I apply Data Mesh principles to build scalable, governed platforms where AI, analytics, and graph capabilities converge to deliver real business insight.