Job Title: Expert D&T;
- Senior Data Modeler
- FinanceLocation: Barcelona, SpainJoin us as a highly motivated and detail-oriented Semantic Data Modeler in the finance data domain. In this role, you will design, develop, and maintain semantic data models that form the backbone of intelligent data integration, provide context for AI and build an important pillar of data quality. In this role, you will design, govern, and evolve enterprise data models, with a focus on the finance data domain. You will work in a green field, emphasizing on finance and considering the big picture of supply chain management; you will deliver semantics for the data in finance for data engineering to implement the corresponding Vault. You will participate in the specification of standards for semantics and enterprise graphs. In this position you will collaborate with other data domains, define and enforce modeling standards, naming conventions, documentation practices and tools supporting the development and consumption of semantic models. This is an incredible opportunity to grow in an organization where critical thinking, out of the box thinking and capacity to deliver are highly appreciated. In this position you will help in building the dsm-firmenich enterprise semantic layer. .
Your

key responsibilities
Own the semantic data modeling end to end life cycle. From use case specification, design, implementation, quality assurance for the resulting data models and AI readiness. Facilitate technical alignments across domains, interaction with the vault layer, ensuring historical correctness, structural integrity, and fitness for analytical consumption, designing and specifying data analysis workflows. Define and govern the modeling approach determining the structure at each layer based on business and technical requirements.




Deliver precise implementation-ready model specifications for data engineering teams.
Translate complex business requirements into formal semantic data models, working directly with subject matter experts and product owners to capture and represent domain semantics accurately.
Collaborate with BI and analytics teams to validate that consumption-ready structures effectively meet reporting and analytical requirements.
Define and enforce modeling standards, naming conventions, and metadata documentation practices, maintaining a living catalog of enterprise data models.
Collaborate with data engineers, architects, and platform teams to implement and operationalize data models in modern cloud and lakehouse environments.
Apply and help in defining data modeling standards and best practices, including metadata management, semantic consistency, data lineage, and reusable data design.
Promote data quality, consistency, and governance by establishing common definitions, business rules, and traceability across data products.
Drive continuous improvement and innovation by simplifying data structures, advancing modeling practices, and enabling analytics, reporting, and AI use cases.

We offer
Unique career paths across health, nutrition and beauty, explore what drives you and get the support to make it happen.
A science led company, cutting edge research and creativity everywhere, from biotech breakthroughs to sustainability game-changers,



you'll work on what's next.
Growth that keeps up with you, you join an industry leader that will develop your expertise and technical depth.
A culture that lifts you up, with collaborative teams, shared wins, and people who cheer each other on.
A community where your voice matters, it is essential to serve our customers well.
You bringProven ability to implement the enterprise modeling lifecycle — from facilitating conceptual sessions with business stakeholders and domain experts, through logical design, down to physical model specifications — ensuring every layer reflects both business intent and technical feasibility.
Familiarity with Data Vault 2.0 and understanding of dimensional modeling for analytical consumption. Solid understanding of Medallion Architecture and how modeling responsibilities map to each layer.
Proven ability to own and govern data models across an integrated historized layer and a business-facing consumption layer, maintaining clear structural contracts between them.
Ability to produce unambiguous, implementation-ready model specifications that data engineering teams can implement and execute without requiring constant clarification.
Experience with data modeling, not restricted to SQL but ideally with graph data structures and ontologies –RDF, OWL. JSON, NEO4J, etc. Proficiency with data modeling tools and familiarity with how models translate into ETL/ELT patterns, without owning their development. Excellent documentation and communication skills to articulate modeling decisions to both technical and non-technical audiences.
Experience with SAP data structures and business object models from a modeler’s perspective: understanding of how SAP organizes key business entities across core mod

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