The Knowledge Graph Engineer will design, develop, and maintain advanced knowledge graph solutions, transforming complex datasets into meaningful, interconnected insights. Leveraging tools like Neo4j, RDF, and ontology modeling, the engineer will play a crucial role in developing Yirifi's risk and compliance knowledge base. Success will be measured by the ability to create scalable graph architectures, ensure data quality, and provide actionable insights to the business.

Responsibilities and Deliverables

Knowledge Graph Development

• Build and maintain knowledge graphs using tools like Neo4j, ensuring robust and scalable graph architectures.
• Implement data models that accurately represent relationships, hierarchies, and ontologies relevant to risk and compliance.

Ontology and Semantic Modeling

• Design and implement ontologies and taxonomies to enhance data discovery and usability.
• Develop and manage RDF schemas and OWL ontologies to support semantic data integration and reasoning.

Data Integration and Transformation

• Integrate structured and unstructured data from diverse sources into the knowledge graph.
• Design ETL pipelines to map and ingest data into graph databases, ensuring accuracy and consistency.

Query Optimization and Analytics

• Develop and optimize complex Cypher, SPARQL, and GraphQL queries for data retrieval and analytics.
• Enable real-time insights by implementing efficient query performance strategies.

Data Quality and Governance

• Implement validation rules and mechanisms to ensure the integrity and accuracy of the knowledge graph.
• Establish metadata standards and governance practices for the graph database.

 Collaboration and Stakeholder Engagement

• Partner with data scientists, product teams, and compliance experts to align graph solutions with business objectives.
• Translate technical concepts into actionable insights for stakeholders.

Emerging Technologies and Best Practices

• Stay abreast of advancements in knowledge graph technologies, ontology modeling, and semantic web standards.
• Pilot and implement innovative tools to enhance the knowledge graph ecosystem.

 Key Performance Indicators (KPIs)

• Graph Completeness: Achieve 95% representation of key entities and relationships in the knowledge graph.
• Query Performance: Ensure query response times are under 2 seconds for 90% of queries.
• Data Quality: Maintain 99% accuracy in entity relationships and ontology alignment.
• Stakeholder Usability: Ensure the knowledge graph meets defined use cases for analytics, machine learning, or compliance reporting.
• Metadata Coverage: Ensure 100% of graph nodes and edges have associated metadata.

Graph Databases:

• Expertise in Neo4j, including Cypher query language and graph data modeling.
• Familiarity with other graph databases (e.g., TigerGraph, Amazon Neptune) is a plus.

Ontology and Semantic Web:

• Strong understanding of RDF, OWL, and SPARQL.
• Experience in designing and managing ontologies and taxonomies.

Data Integration:

• Hands-on experience with ETL tools and frameworks for graph data pipelines.
• Ability to process and transform unstructured data into graph-compatible formats.

Programming:

• Proficiency in Python or Java for scripting and integrating with graph databases.
• Experience with RESTful APIs for accessing and managing graph data.

Data Quality and Governance:

• Familiarity with tools and frameworks for data validation and lineage tracking.
• Understanding of data governance best practices for graph databases.

 Soft Skills

• Strong problem-solving and analytical thinking abilities.
• Excellent communication skills for explaining technical solutions to non-technical stakeholders.
• Ability to work collaboratively in a dynamic, fast-paced environment.
• Attention to detail and a commitment to delivering high-quality results.

• 3+ years of expertise in Neo4j, including Cypher query language and graph data modeling.