Knowledge Graph Engineer (Life Sciences)

CAPGEMINI ENGINEERING

At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same.

YOUR ROLE

You will build and maintain the scientific knowledge graph content that will be used to answer scientific questions. You turn biological concepts into ontology-aligned RDF models, write the Python pipelines that generate them, and keep every assertion traceable to its source and evidence. You bridge scientific meaning and technical delivery, so that graph assets are accurate, governed and usable by analytics and AI applications.

Key responsibilities

• Design RDF-based data models and reusable semantic structures for genes, proteins, diseases, phenotypes, compounds and experiments
• Map structured and unstructured data to controlled 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, test and analyze knowledge graphs
• Implement automated semantic quality checks and mapping validation (SHACL or equivalent)
• Collaborate with scientists and ontology specialists to resolve terminology, modelling and data-quality questions
• Document mapping rules, modelling decisions and validation rules
• Support the integration of graph assets into downstream data, analytics and AI solutions

YOUR PROFILE

• MSc or PhD (or equivalent professional experience) in biology, biomedical sciences, bioengineering, genetics, neuroscience, bioinformatics, computational biology or a related field
• 3 to 5 years of experience in data engineering, software engineering, semantic technologies or scientific data work
• Strong Python and data manipulation skills, and a working command of SQL
• Practical RDF modelling (subject, predicate, object) and hands-on experience with OWL, SPARQL and SHACL, plus RDFS or triple stores
• Experience designing source-to-ontology mappings or integrating multiple scientific data sources
• Practical experience using or developing ontologies, taxonomies, controlled vocabulary or knowledge graphs
• Understanding of provenance, metadata, data quality and schema validation
• Experience with biological or biomedical datasets
• Git and modern software development practices
• Clear communication with scientific and technical stakeholders
• Fluent English
• Preferred qualifications
  • Hands-on experience with Gene Ontology, MONDO, HPO, EFO, UBERON, ChEBI or other OBO Foundry ontologies
  • Semantic-web libraries such as RDFLib or OWLReady2
  • Entity linking, terminology mapping or controlled vocabulary management
  • RDF-star, named graphs or property graph modelling (Neo4j, GraphDB)
  • Knowledge of FAIR principles, scientific metadata governance and LinkML
  • Agentic AI experience & LLM-based agents, retrieval-augmented generation, information extraction or LLM-assisted mapping, with validation of the output
  • Exposure to graph analytics, semantic similarity, machine learning or generative AI
  • APIs, Docker, cloud platforms, workflow orchestration and automated data pipelines
  • Experience in pharmaceutical & healthcare or another regulated environment
  • Portuguese fluency
• What we look for
  • Scientific curiosity paired with engineering discipline
  • Critical thinking and attention to data provenance and quality
  • Comfort with ambiguity, and the ability to move between scientific meaning and technical implementation
  • An impact-driven mindset in a fast-paced environment