Semantic Data Modeler

Migx; Barcelona; Híbrido; EUR 60.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model

25 holiday days per year

Attractive social benefits package

Career development opportunities

Descripción de la vacante

Migx is seeking a highly motivated Semantic Data Modeler to design, develop, and maintain semantic data models that underpin intelligent data integration and reasoning in life sciences. You will work with RDF/OWL, graph databases, and AI-driven workflows to enable knowledge representation and interoperability.

Responsibilities include modelling, validation, data transformation, governance, and collaboration in an Agile/Scrum environment.

Formación

• Proven expertise in semantic standards (RDF/RDFS/OWL).
• Hands-on with graph databases (GraphDB, Stardog, Neo4j, Neptune).
• Strong SPARQL skills; working knowledge of GraphQL, Cypher, Gremlin.
• Familiarity with SHACL for data validation.
• Solid data modeling principles across relational/NoSQL.
• Experience in ETL/ELT pipelines and NLP-based data integration.
• Experience with AI/ML workflows in semantic pipelines.

Responsabilidades

• Design, develop, and maintain semantic data models for knowledge graphs.
• Manage graph data stores and optimize data pipelines for scale and quality.
• Integrate semantic models into ETL/ELT pipelines and NLP workflows.
• Define governance practices and document modeling decisions and mappings.
• Collaborate in Agile/Scrum environments with cross-functional teams.
• Bring life sciences domain understanding to tailor semantic solutions.

Conocimientos

Semantic standards

Graph databases

SPARQL

Validation SHACL

Data modeling

ETL/ELT

NLP integration

AI/ML workflows

Herramientas

GraphDB

Stardog

Neo4j

Amazon Neptune

GraphQL

Cypher

Gremlin

Descripción del empleo

About the profile

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.

Responsibilities

1. 1. Semantic Data Modeling 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.
2. 2. Graph Data Management 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.
3. 3. Data Transformation & Integration 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.
4. 4. Quality & Governance 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.
5. 5. Collaboration & Continuous Improvement 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.
6. 6. Business Acumen 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.

Requirements - Must have

Hard skills:

• 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.

Soft skills:

• 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.

Requirements - Nice to have

• 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.

Languages

English: B2/C1 level.

What we offer

• Hybrid work model and flexible working schedule that would suit night owls and early birds.
• 25 holiday days per year.
• Attractive social benefits package.
• Opportunities for career development and the opportunity to shape the company's