Life Sciences Knowledge Graph Engineer - Hybrid

Capgemini Engineering

Fundão

Hybrid

EUR 45,000 - 60,000

Full time

12 days ago

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Benefits offered by this job

Hybrid work environment

Health and life insurance

Training and certifications programs

Referral program with bonuses

Job summary

Capgemini Engineering is seeking an experienced data engineer / semantic technologies specialist to build and maintain a scientific knowledge graph. You will convert biological concepts into ontology-aligned RDF models, develop Python pipelines, and ensure traceability to sources and evidence for analytics and AI applications.

Responsibilities include designing RDF data models, data mapping to ontologies, and implementing quality checks.

Qualifications

MSc or PhD (or equivalent) in biology or related field.
3–5 years in data engineering, software engineering, semantic technologies or scientific data work.
Strong Python and data manipulation skills with SQL.
Proficient RDF modelling (subject, predicate, object) and hands-on with OWL, SPARQL and SHACL; RDFS or triple stores.
Experience designing source-to-ontology mappings or integrating multiple scientific data sources.
Experience with ontologies, taxonomies, controlled vocabulary or knowledge graphs.
Understanding of provenance, metadata, data quality and schema validation.
Experience with biological/biomedical datasets.
Git and modern software development practices.
Clear communication with scientific and technical stakeholders.
Fluent English.
Preferred: Gene Ontology, MONDO, HPO, EFO, UBERON, ChEBI; RDFLib, OWLReady2; RDF-star; FAIR principles; knowledge of LLM-based agents; APIs, Docker, cloud platforms; regulated environment; Portuguese fluency.

Responsibilities

Design RDF-based data models and reusable semantic structures for genes, proteins, diseases, phenotypes, compounds and experiments.
Map structured and unstructured data to vocabularies and ontologies.
Develop Python pipelines for data transformation, entity resolution and RDF generation.
Define and validate identifiers, relationships, provenance and evidence.
Write SPARQL queries to explore and analyze knowledge graphs.
Implement semantic quality checks and mapping validation (SHACL or equivalent).
Collaborate with scientists and ontology specialists to resolve terminology and data-quality questions.
Document mapping rules, modelling decisions and validation rules.
Support integration of graph assets into downstream data, analytics and AI solutions.