Data Modeler (openEHR / Health Data)

The Health Data Service (HDS) unit builds IT and data solutions that support registries, data collection platforms, and research studies at LIH. We work closely with researchers, principal investigators, and national partners such as the Ministry of Health.
The Data Modeler will play a central role in shaping how health data is structured, standardised, and made interoperable across LIH. openEHR is the institutional standard for clinical data modelling, and this person will be the in-house expert responsible for designing, governing, and evolving LIH’s openEHR archetype and template library. Within a matrix-based organisation, the Data Modeler supports the Health Data Standard project and the broader Health Data Foundry vision. While openEHR is the core of the role, the work also involves relational data modelling, FHIR resource mapping, and ensuring alignment between clinical models and the underlying data infrastructure.

Key Accountabilities

• Design, build, and maintain clinical and research data models using openEHR archetypes and templates, as well as relational and FHIR-based representations where appropriate
• Collaborate with clinical partners, researchers, and data stewards to capture data requirements and translate them into formal, reusable data models
• Lead the definition and implementation of Health Data Standards across LIH research projects
• Ensure alignment of data models with international standards (openEHR, HL7 FHIR, SNOMED CT, ICD, LOINC) and institutional data governance policies
• Work with the data engineering team to ensure models are implementable in production environments, including the clinical data repository and data warehouse
• Design and maintain relational database schemas that support application and reporting requirements
• Contribute to the governance of the clinical data repository and its metadata catalogue
• Document modelling decisions and maintain a versioned library of archetypes, templates, and schema definitions
• Participate in cross-functional coordination sessions and contribute to team-level planning and knowledge sharing
• Engage with the international openEHR community and contribute to shared model development

Key Skills, Experience and Qualifications

Qualifications

• Master’s degree in Medical Informatics, Health Information Management, Computer Science, or a related field
• Minimum 5 years of experience in clinical or health data modelling, with demonstrable openEHR expertise
• Deep understanding of the openEHR Reference Model, archetypes, templates, compositions, and the Archetype Definition Language (ADL)
• Hands-on experience building and publishing openEHR archetypes and templates in a production or standards-development context
• Experience with relational data modelling and database design (PostgreSQL, SQL Server, or comparable)
• Familiarity with health data interoperability standards such as HL7 FHIR, SNOMED CT, LOINC, and ICD
• Experience working with clinicians or clinical workflows to elicit and formalise data requirements
• Understanding of both relational and document-based data storage approaches
• Working knowledge of clinical data repositories or EHR systems
• Experience with tooling such as the openEHR Clinical Knowledge Manager, Archetype Designer, or similar
• Fluent in English (written and oral); French or German is an asset
• Experience in healthcare, public health, or health data management is a strong asset
• Experience working in agile environments; ability to coordinate technical delivery across cross-functional and matrix-based teams

Required Tech Stack

• openEHR tooling: Archetype Designer, Clinical Knowledge Manager, EHRbase or comparable CDR, AQL (Archetype Query Language)
• openEHR operational templates, template data documents (TDD), and composition validation
• HL7 FHIR, SNOMED CT, LOINC, ICD terminology and mapping tools
• SQL, relational database design (PostgreSQL, SQL Server)
• Data modelling tools (ER diagrams, UML, or domain-specific tooling)
• Git for version control of models and templates
• Familiarity with ETL/data pipeline concepts is an advantage