Semantic Data Modeler

• Proven extensive expertise in semantic standards: RDF, RDFS, OWL, SKOS/SKOS-XL, DCT, DCAT.
• Hands-on experience with graph databases (e.g., GraphDB, Stardog, Neo4j, Amazon Neptune).
• Strong proficiency in SPARQL; working knowledge of GraphQL, Cypher, or Gremlin.
• Familiarity with validation languages: SHACL is a must have.
• Solid understanding of data modeling principles and relational/NoSQL systems.
• Experience in ETL/ELT pipelines, NLP-based data integration, and data quality management.
• At ease with AI/ML workflows and the use of LLMs for semantic work.
• Attention to detail and commitment to high-quality deliverables.
• Natural ability to connect the dots across abstract and concrete concepts.
• Strong working/semantic memory and ability to juggle complex model dependencies.
• A visual/graphical/3D thinking brain: comfortable conceptualizing networks, hierarchies, and relationships.
• Excellent documentation and communication skills: able to explain semantic concepts clearly to senior management.
• Proactive, curious, and eager to explore and adopt emerging semantic technologies.
• Inventive: propose innovative approaches to model and manage data using graphical approaches.

• Highly organized with strong personal knowledge management practices.
• Collaborative team player with the ability to influence and support other.
• A problem-solver who thrives in complex, multidisciplinary environments.
• Enthusiastic about life sciences, biotech, or pharma, with a desire to support research and innovation.

English: B2/C1 level.

We are seeking a highly motivated and detail-oriented Semantic Data Modeler to join our innovative team. In this role, you will design, develop, and maintain semantic data models that form the backbone of intelligent data integration, knowledge discovery, and reasoning in complex domains such as life sciences. This position sits at the crossroads of data modeling, semantic standards, graph technologies, and AI-driven workflows, enabling advanced knowledge representation and data interoperability.

If you are passionate about connecting the dots across complex data ecosystems, thrive on precision and structure, and have a natural ability to think in terms of graphs, hierarchies, and relationships, this role is for you.

This is an opportunity to be at the forefront of the semantic data revolution in life sciences, helping organizations turn complex data into actionable knowledge. You will work with cutting-edge tools and contribute to impactful projects that accelerate discovery, innovation, and better outcomes for patients worldwide.

Design and maintain ontologies and vocabularies using RDF, RDFS, OWL, SKOS/SKOS-XL, DCT, DCAT, and many other standard ontologies.

In collaboration with Business Analysists, translate business concepts into semantic models that support interoperability and reasoning.

Ensure semantic models align with web standards (W3C, FAIR) and industry standards (CDISC, SNOMED CT, etc)

Apply validation logic and language (SHACL or ShEx) to ensure model and data quality.

Model, store, and query data in graph databases (triple stores and property graph systems).

Use SPARQL and other graph query languages (e.g., GraphQL, Cypher, Gremlin) to retrieve and manage knowledge graphs

Optimize graph data pipelines for scalability, performance, and accuracy.

Design models as integrative parts of implement ETL/ELT pipelines to automatize knowledge extraction from structured and unstructured data into semantic/graph-based systems.

Integrate semantic models to NLP techniques for semantic enrichment/annotation of text-derived knowledge.

Collaborate with data engineers and scientists to ensure smooth ingestion and alignment of heterogeneous datasets.

Define semantic data governance practices ensuring consistency, traceability, and reusability.

Document modeling choices, schema evolution, and semantic mappings thoroughly.

Contribute to metadata standards and data stewardship practices.

Work in an Agile/Scrum environment, delivering iterative improvements.

Collaborate closely with cross-functional teams (data engineers, scientists, domain experts, AI specialists).

Contribute to DevOps and MLOps practices in semantic pipelines.

Bring a as much understanding of the life sciences as possible, particularly in the context of biotech, and pharma sectors, to tailor solutions to industry-specific needs.