Knowledge Engineer, Back End

What you'll do

• Map source data to ontology models to hydrate structured and semi-structured information into Knowledge Graphs.
• Develop data mapping and transformation workflows using R2RML or similar technologies.
• Write and optimize SPARQL queries for graph loading, validation, and retrieval.
• Build and maintain automated data ingestion pipelines from various enterprise systems.
• Design and optimize relational database schemas and query patterns to support ETL workflows.
• Deploy and manage vector database infrastructure for embedding storage and semantic retrieval at scale.
• Develop and maintain scalable REST APIs for internal application and product teams.
• Manage the containerization, deployment, and monitoring of graph services in cloud environments.

What we're looking for

• Must have a Bachelor's degree or equivalent of 12 years of experience; an Associate's degree requires 6 years of experience.
• Minimum 3 years of experience in Knowledge Graph data hydration and ontology-based data mapping using RDF, SPARQL, and semantic technologies.
• Minimum 3 years of experience with R2RML or similar frameworks for transforming relational data into graph models.
• Minimum 3 years of experience with graph databases (e.g., StarDog, GraphWise, Neo4J) and Elasticsearch/OpenSearch.
• Minimum 3 years of experience with relational databases including schema design, indexing, and query optimization.
• Minimum 3 years of experience deploying and operating vector databases in production environments.
• Minimum 3 years of proficiency in Python or Java for automation, integration, and designing REST APIs.
• Must be able to travel between 20% and 80% of the time to client locations.