Neo4j Graph Ontology Specialist

Role: Neo4j Graph Ontology Specialist

Mode: Fulltime

Location: NJC, NJ or Mississauga, ON (Locals Only)

Neo4j Graph Ontology: A Neo4j graph ontology skill set profile involves modeling skills as a graph database where skills, roles, and people are nodes and relationships are the connections between them. A core skill set includes knowledge of Cypher for querying, Neo4j's property graph model for structuring data, and the ability to define an ontology to give the graph a formal, actionable structure. To create a profile, you would use skills like data modeling, ontology engineering, query profiling, and potentially Python for integration

Core skill set:

• Cypher: This is Neo4j's native graph query language. The ability to write and understand Cypher is essential for querying the graph to find relationships and patterns in skills.
• Graph Data Modeling: Understanding how to model complex relationships in a graph is fundamental. This includes defining node labels (e.g., Person, Skill, JobRole) and relationship types (e.g., HAS_SKILL, REQUIRES_SKILL, WORKS_ON).
• Ontology Design: This involves formally defining the domain model, including the types of entities, their properties, and the relationships between them. This provides a structured framework for building the graph and ensuring consistency.
• Query Profiling: A key skill is the ability to profile query execution to identify bottlenecks and optimize performance, often by adding PROFILE to a Cypher query to see the execution plan.
• Integration: Proficiency in languages like Python is valuable for writing scripts to load data into the graph, build the graph based on the ontology, and integrate Neo4j with other systems.

• Skill ontology engineering: This is the specific application of ontology design to the skill domain, focusing on creating a formal and dynamic model for skills, their adjacencies, and their relationships to roles and projects.
• Knowledge Graph Construction: The overall process of building a knowledge graph from raw data, guided by the ontology, is a key skill.
• Graph algorithms: The ability to apply algorithms (e.g., shortest path, community detection) to the graph can uncover deeper insights into skill relationships and talent pools.
• AI/ML integration: Leveraging the skill ontology to train AI/ML models for tasks like resume screening, job matching, and skills gap analysis is an increasingly important skill.
• Data Governance and Security: Applying ontology reasoning to check the consistency of security rules applied to the graph database is a specific and valuable skill.