Graph database development on Neo4j and Cypher modeling

I design and build graph databases on Neo4j for products where relationships between entities matter more than the entities themselves. If you catch yourself doing JOINs across five tables, watching a query crawl for a minute, and still can't answer questions like "who is two steps away from a suspicious account" — that's a sign a graph fits better than tables. I start with your real questions, turn them into a clean schema of nodes and relationships and validate it on your own data with Cypher queries before writing a line of application code.

My work covers the full path: modeling entities and relationships, choosing properties and indexes, tuning Cypher so traversals stay fast as the graph grows, and importing data from SQL dumps, CSV or event streams without losing history. On top of the database I build a clean API so the application talks to the graph through stable endpoints, not raw queries. I've launched recommendation engines, fraud detection backends and network analysis systems, knowledge graphs and identity resolution systems where a single traversal replaces dozens of table queries.

You get a documented schema, a library of queries with analyzed execution plans, a repeatable import pipeline and an API layer with tests. If you're not sure a graph is the right tool, send me your heaviest query and I'll show you what it looks like as a graph traversal.