Duties and Responsibilities:
- As a Neo4J Lead, you will be responsible for development, support, maintenance, and implementation of enterprise-grade graph data models in Neo4j, ensuring scalability, consistency, and alignment with business domains such as customer 360, fraud detection, supply chain, or knowledge graphs.
- Design and implement Neo4j architecture models including clustering, indexing strategies, query optimization, caching, and high-availability deployments to support large-scale graph workloads and real-time analytics.
- Establish standards and best practices for Cypher queries, GraphQL integrations, APOC procedures, and API design to ensure maintainability, security, and performance across engineering teams.
- Drive adoption of Graph Data Science (GDS) capabilities for recommendations, pathfinding, community detection, and AI-powered insights by collaborating with data science and AI engineering teams.
- Analyzing user requirements, and defines technical project scope and assumptions for assigned tasks.
- Creating technical designs for new systems, and/or modifications to existing systems.
- Translating detailed requirements into functional system designs.
- Prioritizing work, meeting deadline and also establishing and maintaining effective working relationships with clients, project team members, supervisors, and employees from other departments.
- Integrate Neo4j with cloud-native platforms, streaming systems (Kafka), vector databases, and enterprise data lakes to support hybrid architectures and next-generation intelligent applications.
- Partner with business leaders, enterprise architects, and product owners to identify new graph-based use cases, evaluate emerging technologies, and align Neo4j initiatives with digital transformation goals.
Requirements:
- At least 8+ years of overall experience in designing and developing Neo4j-based Knowledge Graph and graph data solutions using enterprise integration and distributed architectures.
- Hands-on experience working with Neo4j, Cypher Query Language, APOC procedures, GraphQL, RDF/SPARQL concepts, and semantic data modeling for connected data platforms.
- Strong experience in building and managing teams with expertise in Knowledge Graphs, including ontology modelling, metadata management, graph schema design, entity relationship mapping, and graph traversal optimization.
- Hands-on experience integrating Neo4j with modern technologies such as Microservices, Kafka, REST APIs, Spring Boot, OpenShift, Kubernetes, and cloud-native platforms.
- Experience implementing Graph Data Science (GDS) algorithms including pathfinding, similarity analysis, recommendation engines, community detection, and network analytics.
- Good to have hands-on experience integrating Knowledge Graphs with AI/ML and Generative AI frameworks, including Retrieval-Augmented Generation (RAG), semantic search, vector embeddings, and intelligent recommendation systems.
- Experience in building scalable and containerized graph applications using Docker, OpenShift Containers, Kubernetes, Jenkins, GitHub, and CI/CD pipelines.
- Strong understanding of graph database performance tuning, indexing strategies, query optimization, clustering, and high-availability deployment architectures in Neo4j.