Senior Biomedical Data Modeller - FAIR Knowledge Graph

Capgemini; Évora; Hybrid; EUR 60,000 - 90,000; Full time

Benefits offered by this job

Hybrid work

Health and life insurance

Career growth programs

Training and certifications

Referral program

Job summary

Capgemini is seeking an experienced modelling lead to shape a federated data architecture for biomedical data. You will translate complex scientific requirements into canonical models, define identifiers, provenance, and governance across data assets, and drive schema lifecycle with GitHub.

Collaboration spans biology SMEs, data engineering, platform teams, and governance groups. You will mentor junior modelers and ensure alignment with FAIR principles, ontologies, and enterprise standards while

Qualifications

• MSc or PhD in biology or related field.
• 6+ years of experience in scientific/biomedical data modelling.
• Strong modelling concepts for complex biomedical domains.
• Experience with LinkML or equivalent schema framework.
• Data harmonization, provenance and FAIR principles.
• Validation rules and data quality checks experience.
• Knowledge of ontologies and vocabularies.
• GitHub-based schema governance and versioning.
• Proficient in Python and SQL.
• Excellent stakeholder management; fluent English.

Responsibilities

• Lead modelling strategy across data harmonization, pipeline validation, knowledge graph and FAIR data product requirements.
• Translate ambiguous scientific requirements into clear conceptual, logical and canonical data models.
• Model biomedical entities and relationships including genes, targets, diseases, indications, phenotypes, mechanisms, biomarkers, tissues, cell types, assay modalities, evidence, provenance and causal relationships.
• Make ontology reuse, extension and mapping decisions, with documented rationale.
• Define persistent identifiers and consistent provenance fields across data assets.
• Drive schema review, approval, versioning and publication processes in GitHub, including changelogs, tagged releases and downstream impact assessment.
• Define validation rules and data quality checks for ontology terms, ranges, required fields, ID and label consistency, cross-field consistency and provenance completeness.
• Identify modelling risks early: metadata gaps, ontology conflicts, source data quality issues, lineage gaps and downstream compatibility problems.
• Design modular, reusable and future-proof models aligned to FAIR principles and enterprise standards.
• Work with scientific knowledge engineering, biology SMEs, data engineering, knowledge graph engineering, product management, data stewards and platform teams.
• Mentor junior and mid-level modelers.

Job description

Capgemini is seeking an experienced modelling lead to shape a federated data architecture for biomedical data. You will translate complex scientific requirements into canonical models, define identifiers, provenance, and governance across data assets, and drive schema lifecycle with GitHub.

Collaboration spans biology SMEs, data engineering, platform teams, and governance groups. You will mentor junior modelers and ensure alignment with FAIR principles, ontologies, and enterprise standards while