Graph-Driven Semantic Data Architect | Hybrid & Flexible

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

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.