Ben Squire

Ben Squire is a senior developer advocate at Neo4j specializing in graph data science, identity resolution, and enterprise AI. His work applies relationships between fragmented records to problems ranging from audience analytics to AI agents navigating complex business data.

Previously a senior data scientist at Meredith Corporation, Squire worked on audience analytics, recommendation systems, and customer segmentation. Meredith’s graph-based audience analytics connected cookies and behavioral records into pseudonymous profiles, using community detection to identify related audiences and assess questionable third-party data.

At Neo4j, his focus expanded to knowledge graphs, developer advocacy, and enterprise data platforms, including graph analytics and customer segmentation for Snowflake. His SumoDB writing applies graph modeling and analytics to professional sumo.

• Identity resolution through connected behavior: Graph relationships and community detection help connect fragmented audience signals and expose unreliable records.
• Graph analytics inside existing data platforms: Snowflake-centered workflows make relationship analysis and customer segmentation accessible within established enterprise data environments.
• Graph-shaped context for enterprise AI: At AI Engineer World’s Fair 2026, Squire supported a lakehouse AI workshop alongside Zach Blumenfeld and Ryan Knight. The team demonstrated how warehouse metadata, document hierarchies, cross-references, and thematic clusters can help agents answer questions across structured and unstructured information without necessarily copying operational records into another database. Blumenfeld led the technical presentation; Squire helped attendees work through the material.
• SumoDB: Squire uses relationships among competitors and events to make graph modeling concrete through an unconventional real-world dataset.