Semantic Data Modeler at MIGx

Barcelona, Catalonia, Spain -

Full Time

Start Date

Immediate

Expiry Date

18 Oct, 26

Salary

0.0

Posted On

20 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Semantic Data Modeling, RDF, RDFS, OWL, SKOS, SPARQL, SHACL, Graph Databases, Knowledge Graphs, ETL/ELT Pipelines, NLP, Data Governance

Industry

IT Services and IT Consulting

Description

JD ID Position Name 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. 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. 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. 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. 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. 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. 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. Seniority Level 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 future. An employee-centric culture directly inspired by employee feedback - your voice is heard, and your perspective encouraged. Different training programs to support your personal and professional development. Work in a fast growing, international company. Friendly atmosphere and supportive Management team. About us MIGx is a global consulting company with an exclusive focus on the healthcare and life science industries, with their particularly demanding requirements on quality and regulatory aspects. We have been managing challenges and solving problems for our clients in the areas of compliance, business processes and many others. MIGx interdisciplinary teams from Switzerland, Spain and Georgia have been taking care of projects in the fields of M&A, Integration, Application, Data Platforms, Processes, IT management, Digital transformation, Managed services and compliance.

Responsibilities

Design and maintain semantic data models and ontologies to enable intelligent data integration and reasoning in the life sciences domain. Manage graph databases and implement ETL pipelines to automate knowledge extraction from structured and unstructured data.