Description :

Role Overview :

We are seeking a Neo4j Graph Database Engineer to design, implement, and optimize graph-based data models for advanced copay analytics. This role will support anomaly detection, patient journey mapping, and provider-pharmacy influence analysis using Neo4j and Apache Spark at scale.

Key Responsibilities :

- Design and implement graph data models based on relational copay data, including entities like Patients, Transactions, Providers, and Pharmacies.

- Use the Neo4j Connector for Apache Spark to convert and batch-load large datasets into the graph.

- Optimize graph ingestion using UNWIND, MERGE, and APOC procedures.

- Implement graph algorithms (PageRank, Degree Centrality, Community Detection, etc.) for insights and anomaly detection.

- Enable real-time data updates and alerting mechanisms for live copay activity using Neo4j triggers or streaming tools.

- Collaborate with data scientists to support Graph ML/AI model development (e.g., GNN, One-Class SVM).

- Visualize graph data using Neo4j Bloom to enable intuitive insights for business stakeholders.

- Document infrastructure, modeling, and ETL processes in Confluence.

Must-Have Skills :

- Strong hands-on experience with Neo4j (Enterprise preferred) and Cypher query language

- Proficiency with Apache Spark, including Spark-GraphX or Neo4j-Spark connector

- Experience in data modeling from RDBMS to graph structures

- Familiarity with graph algorithms for influence, similarity, community detection, and anomaly scoring

- Proficient in scripting and data transformation using Python / PySpark

- Experience with APOC procedures, UNWIND, and batch ingestion techniques

- Comfortable working with AWS EC2/EBS, and large-scale datasets (millions of nodes/edges)