Highlights
Working knowledge of major life sciences ontologies: Gene Ontology (GO), OBO Foundry ontologies (CL, UBERON, HPO, MONDO, CHEBI, EFO, CLO), MeSH, SNOMED CT, UMLS. Hands-on experience with open-source ontology tools and languages: Protégé, SPARQL, OWL, SKOS, SHACL, RML, RDF-start.
Numbers & Facts
| Location | New York, NY (Remote) |
| Industry | Other/Not Classified |
| Salary | $60–$70 Per Hour |
| Company Size | 100 to 499 employees |
| Year Founded | 1998 |
| Website | www.eteaminc.com |
Description
Role Name: Knowledge Engineer
Work site: Remote
Basic Qualifications:
We are looking for professionals with the required skills to achieve our goals:
•
Masters degree in BioSciences, Biomedical Science, Biomedical Engineering, Biotechnology (with a life science/pharma application focus)
•
6 years of relevant knowledge graph work experience
•
Specific hands-on experience contributing to Knowledge Graph development efforts, including entity modeling, relationship design, r2rml, and schema governance
•
Hands-on experience with open-source ontology tools and languages: Protégé, SPARQL, OWL, SKOS, SHACL, RML, RDF-start
•
Working knowledge of major life sciences ontologies: Gene Ontology (GO), OBO Foundry ontologies (CL, UBERON, HPO, MONDO, CHEBI, EFO, CLO), MeSH, SNOMED CT, UMLS
•
Familiarity with linked data principles and semantic web technologies
•
Demonstrated experience with industry-standard tools for building data serialization protocols (e.g., JSON Schema, LinkML)
•
Proficiency in at least one programming language — preferably Python and LLM— for scripting vocabulary mappings, building data models, automating QC, and prototyping pipelines
Preferred Qualifications:
If you have the following characteristics, it would be a plus:
•
Experience with data governance and data quality tooling (e.g., Ataccama, Informatica, Talend, OpenRefine, Great Expectations, dbt)
•
Experience with at least one programming language – e.g. Python – for scripting vocabulary mappings, building data models, etc
•
Experience supporting LLM integration or AI-readiness workflows — including metadata enrichment, entity linking, embedding pipelines, or retrieval-augmented generation (RAG) architectures
•
Understanding of vector databases and their role in semantic search and knowledge retrieval (e.g., Weaviate, Chroma)
•
Familiarity with cloud data platforms and infrastructure relevant to large-scale biological data (e.g., AWS, GCP, Azure)
•
Familiarity with graph database technologies (e.g., Neo4j, Amazon Neptune, Stardog, GraphDB, TigerGraph)
Work site: Remote
Basic Qualifications:
We are looking for professionals with the required skills to achieve our goals:
•
Masters degree in BioSciences, Biomedical Science, Biomedical Engineering, Biotechnology (with a life science/pharma application focus)
•
6 years of relevant knowledge graph work experience
•
Specific hands-on experience contributing to Knowledge Graph development efforts, including entity modeling, relationship design, r2rml, and schema governance
•
Hands-on experience with open-source ontology tools and languages: Protégé, SPARQL, OWL, SKOS, SHACL, RML, RDF-start
•
Working knowledge of major life sciences ontologies: Gene Ontology (GO), OBO Foundry ontologies (CL, UBERON, HPO, MONDO, CHEBI, EFO, CLO), MeSH, SNOMED CT, UMLS
•
Familiarity with linked data principles and semantic web technologies
•
Demonstrated experience with industry-standard tools for building data serialization protocols (e.g., JSON Schema, LinkML)
•
Proficiency in at least one programming language — preferably Python and LLM— for scripting vocabulary mappings, building data models, automating QC, and prototyping pipelines
Preferred Qualifications:
If you have the following characteristics, it would be a plus:
•
Experience with data governance and data quality tooling (e.g., Ataccama, Informatica, Talend, OpenRefine, Great Expectations, dbt)
•
Experience with at least one programming language – e.g. Python – for scripting vocabulary mappings, building data models, etc
•
Experience supporting LLM integration or AI-readiness workflows — including metadata enrichment, entity linking, embedding pipelines, or retrieval-augmented generation (RAG) architectures
•
Understanding of vector databases and their role in semantic search and knowledge retrieval (e.g., Weaviate, Chroma)
•
Familiarity with cloud data platforms and infrastructure relevant to large-scale biological data (e.g., AWS, GCP, Azure)
•
Familiarity with graph database technologies (e.g., Neo4j, Amazon Neptune, Stardog, GraphDB, TigerGraph)
About Company
Looking for a great job? Join eTeam. We’re looking for talented staffing professionals to join our staff. We also provide contract assignments and full-time jobs at Fortune 2000 Companies. We’ve been named one of the best companies to work for by Staffing Industry Analysts and New Jersey Business.