Graph Databases: 4+ years of dedicated hands-on experience with Neo4j (Enterprise Edition, AuraDB, or Aura Analytics) and deep mastery of the Cypher query language.
Graph Analytics: Extensive experience utilizing the Neo4j Graph Data Science (GDS) library to implement graph-native unsupervised/supervised ML, link prediction, and node classification.
Core Languages: Strong proficiency in Python (PyData stack, graph-data-science client) and/or Java (for custom stored procedures and extension development).
AI & NLP: Experience with Knowledge Graph generation, Vector Search, and orchestration tools for GraphRAG or agentic AI patterns.
Cloud & Data Ecosystem: Proven experience with cloud data architectures (AWS, Azure, or GCP) and integrations with modern data lakes/warehouses (Snowflake, Databricks, BigQuery, or Spark).