Neo4j Graph Ontology Specialist (NJC, NJ or Mississauga, ON. )

Role- Neo4j Graph Ontology Specialist Mode- Fulltime, Hybrid Location- NJC, NJ or Mississauga, ON. ( Locals Only) Skill Years of Experience Neo4j Cypher Graph Data Modeling Python Data Governance and Security AI/ML • 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. Related and advanced skills • 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.