Senior Neo4j Graph Data Science (GDS) Developer

Requirements

• Graph Mindset: The ability to look at traditional tabular/relational business data and intuitively map it out as interconnected networks.
• Problem Solver: Excellent analytical skills to troubleshoot memory allocation errors, long-running queries, or complex graph projection challenges.
• Strong Communicator: Ability to explain complex graph topologies and ML metrics clearly to non-technical business stakeholders.
• 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).
• DevOps & Infrastructure: Solid understanding of Docker, Kubernetes, and deployment configurations for distributed graph systems.
• Day-to-Day Activities:
• Participating in daily stand-up meetings and project planning sessions.
• Collaborating with cross-functional teams to understand business requirements and design solutions.
• Writing, testing, and deploying software solutions.
• Participating in code reviews and providing feedback to other team members.
• Staying current with the latest technology trends and advancements.
• Providing technical support to team members and resolving technical issues.
• Qualification:
• Bachelor's or Master's degree in Computer Science, Data Science, Data Engineering, Mathematics, or a related quantitative field.
• Neo4j Certified Professional or Neo4j Graph Data Science Certified is highly desirable.
• Soft Skills:
• Problem Solver: Excellent analytical skills to troubleshoot memory allocation errors, long-running queries, or complex graph projection challe

Additional Information

Software Requirements: 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). DevOps & Infrastructure: Solid understanding of Docker, Kubernetes, and deployment configurations for distributed graph systems. Overall Responsibilities: Graph Architecture & Modeling: Lead the design and implementation of highly scalable, enterprise-grade Labeled Property Graph (LPG) data models optimized for both transactional querying and global graph analytics. Graph Data Science & ML: Apply the Neo4j GDS library to execute advanced graph algorithms (e.g., PageRank, Louvain, Weakly Connected Components, Node Embeddings like FastRP) to uncover hidden patterns, fraud rings, or network clusters. Query Optimization: Write, test, and tune complex multi-hop Cypher queries, stored procedures, and User Defined Functions (UDFs) to ensure sub-second response times across billions of nodes and edges. AI & GraphRAG Integration: Architect and integrate knowledge graphs with vector databases, large language models (LLMs), and framework agents using Model Context Protocol (MCP) or LangChain to support accurate, contextual reasoning workflows. Data Ingestion & Pipelines: Design, build, and optimize scalable ETL/ELT pipelines using Apache Spark, Apache Arrow, Kafka, or Neo4j data warehouse connectors to stream and sync data from diverse cloud and on-premise sources. Performance Tuning & MLOps: Manage multi-hop computational graphs in-memory, configure database projections, and scale Neo4j/AuraDB setups for performance, memory footprint tuning, and predictable cloud infrastructure costs. Collaboration & Leadership: Partner with Data Scientists, Software Engineers, and domain experts to align graph design with downstream business intelligence dashboards (NeoDash, Tableau) and enterprise AI solutions. Mentor junior developers on graph thinking.

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