Knowledge Graph Engineer, R&d Data Science & Digital Health - Data Strategy and Products

Johnson & Johnson Johnson & Johnson · Pharma · Madrid, Spain +1

Johnson & Johnson Innovative Medicine is seeking a Knowledge Graph Engineer to standardize and connect biomedical and clinical data. The role involves designing and implementing knowledge graph infrastructure, curating ontologies, applying graph data modeling, and developing services for data ingestion and retrieval. The engineer will partner with teams to enable NLP/RAG over graphs and features for predictive modeling, and work with IT/DevOps for infrastructure deployment and management. Experience with semantic web technologies, graph databases, and biomedical datasets is required.

What you'd actually do

  1. Contribute to the design and implementation of a scalable knowledge graph infrastructure focused on data standardization and interoperability.
  2. Curate and extend ontologies for clear mapping into established biomedical ontologies and controlled terminologies using RDF standards.
  3. Apply graph-based data modeling for efficient organization, integration and retrieval to ensure system flexibility and long-term maintainability.
  4. Stand up SPARQL/GraphQL/REST services; develop ingestion and curation pipelines to ingest, normalize and map concepts across data sources.
  5. Extend and curate ontologies (e.g., diseases, drugs, targets, pathways, etc.) and maintain synonyms, cross-references, and provenance.

Skills

Required

  • semantic technologies
  • ontology
  • graph data modeling
  • biomedical and clinical data
  • RDF standards
  • SPARQL
  • GraphQL
  • REST services
  • ingestion and curation pipelines
  • semantic web technologies
  • RDF Triple Stores
  • property graphs
  • knowledge graphs construction
  • ontology development
  • pharmaceutical or healthcare domains integration
  • graph databases (Neo4j, Amazon Neptune)
  • complex biomedical datasets (genomics, proteomics, high-throughput screening data)
  • data storage solutions (SQL, key-value, column, document, graph stores)
  • data modeling techniques (semantic data, ontologies, taxonomies)
  • CI/CD implementations
  • git usage
  • CI/CD stacks (Jenkins, GitLab, Azure DevOps)
  • DevOps tools
  • metrics/monitoring
  • containerization technologies (Docker, Singularity)
  • analysis
  • problem-solving
  • organizational change
  • project delivery
  • managing external vendors
  • agile decision-making
  • performance management
  • continuous learning
  • commitment to quality
  • multi-task
  • prioritize work
  • organizational skills
  • flexibility
  • translate discussions into user requirements and project plans

Nice to have

  • Advanced Analytics
  • Coaching
  • Critical Thinking
  • Data Analysis
  • Data Privacy Standards
  • Data Quality
  • Data Reporting
  • Data Savvy
  • Data Science
  • Data Visualization
  • Digital Fluency
  • Econometric Models
  • Organizing
  • Process Improvements
  • Strategic Thinking
  • Technical Credibility
  • Workflow Analysis

What the JD emphasized

  • semantic technologies
  • ontology
  • graph data modeling
  • biomedical and clinical data
  • NLP/RAG over graphs
  • predictive modeling
  • knowledge graph construction
  • ontology development
  • pharmaceutical or healthcare domains integration
  • semantic web technologies
  • graph databases

Other signals

  • knowledge graph infrastructure
  • semantic technologies
  • ontology
  • graph data modeling
  • biomedical and clinical data
  • NLP/RAG over graphs
  • predictive modeling