Senior Knowledge Graph Engineer

Remote • Posted 17 hours ago • Updated 17 hours ago
Contract W2
12 Months
Remote
Depends on Experience
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Data Quality
  • Artificial Intelligence
  • Biomedical Engineering
  • JSON
  • Pharmaceutics
  • Life Sciences
  • Ontologies
  • Neo4j
  • Modeling

Summary

Role: Senior Knowledge Graph Engineer

Duration: 12 Months

Location: Remote (For USA)

Domain Life Sciences

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: Protg, 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, Google Cloud Platform, Azure)
  • Familiarity with graph database technologies (e.g., Neo4j, Amazon Neptune, Stardog, GraphDB, TigerGraph)
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 10120856
  • Position Id: 9041258
  • Posted 17 hours ago
Contact the job poster
Ishan Chourasiya

Ishan Chourasiya

Recruiter @ Empower Professionals
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