Ontology and Taxonomy Stewardship: Define and curate enterprise ontologies, taxonomies, controlled vocabularies and semantic layers that connect knowledge across platforms, data products and knowledge graphs.
Transformation to Reusable Semantics: Lead the transformation from project-specific models to reusable enterprise semantic and context assets with clear standards, governance, tooling, adoption pathways and measurable capability uplift.
FAIR/TRUST and Data Mesh Enablement: Champion FAIR and TRUST data principles; align information architecture outputs to a Data Mesh operating model to improve data product discoverability and interoperability.
R&D Strategy Partnership: Work with business leaders to evolve information and semantic designs that support strategy and scientific priorities; own enterprise IA designs for major capability areas and the shared entities and mappings that connect them.
Program Design Authority and Assurance: Take accountability for enterprise IA in major change programmes; assure project designs for conformance to enterprise ontology and metadata standards through blueprints and design reviews.
AI-ready Data and Analytics Patterns: Select and define architectures for reporting, analytics, data science, digital and AI/ML use cases emphasizing semantic consistency, metadata, lineage, quality and interoperability; enable knowledge-graph use cases.
Governance and Education: Lead architectural governance across fields; develop architectural decisions and progress work through forums and councils; evangelize roadmaps and uplift semantic literacy through education, facilitation and coaching.
Data Integration and Reference Models: Support or lead data integration architecture grounded in canonical and semantic reference models; enforce standard enterprise data element names, characteristics and domains across project lifecycles.
Metrics and Data Governance Enablement: Partner with the Data Office to embed governance in IA builds and provide assurance via standardized metrics, including master data consumption, classification metadata, ontology adoption and semantic coverage.
Work Packages and Demand Management: Specify and manage work packages for internal and flexible resources; coordinate demand and recharge in partnership with practice leadership, project managers and business partners.
Essential Skills/Experience
Bachelor’s Degree or equivalent number of years of experience in a Computer Science, Data Management or relevant scientific field.
Experience in leading and delivering enterprise data platform and enterprise information architectural thinking, and its practical application.
Experience in the use of conceptual, logical and semantic data modelling technologies, including ontology and taxonomy design.
Experience in defining and working with information and data regulatory governances (e.g. GxP, privacy, data ethics).
The role holder will possess a blend of data/information architecture, analysis, and engineering skills.
Experience in known industry IT architectural patterns and IT architecture ways of working/methodologies (e.g. FAIR data principles, TRUST, Data Mesh).
Understanding of ontologies, semantic layers, knowledge graphs and canonical information models, and how they enable discoverability, interoperability and AI-ready data.
Understands Data Platforms concepts and cloud-based containerization strategies for hybrid cloud environments.
Understanding the appropriate data structure and technology based on business use case and completely familiar with data lifecycles.
Demonstrated ability to lead semantic modelling transformation and capability uplift, including interested party engagement, operating-model and governance design, change leadership, facilitation, coaching, and practical use of ontology and knowledge-graph standards such as RDF, OWL, SKOS and SHACL.
Desirable Skills/Experience
Post-graduate degree in MIS, Data Management, Knowledge Engineering or equivalent domain.
Extensive experience in a senior information/data architecture role with practical examples of designing and providing end-to-end and point data architecture designs or blueprints that have been delivered and implemented for substantial real world use cases.
Experience of Information and Data Governance frameworks and their application in a commercial or regulated organisation.
Experience in Agile data definition scrums.
Experience in the use of tooling, e.g. metadata cataloguing tools, data modelling tools, ontology/semantic modelling tools (e.g. OWL, RDF, SKOS, SHACL), EA tools, graph platforms.
Understanding of, or familiarity with, Data Mesh approaches (as distinct from Data Fabric or Data Platform).
Familiarity with industry/pharma standards and ontologies (e.g. CDISC, IDMP, SNOMED CT, MedDRA, LOINC, UMLS, ChEBI, OMOP, HL7 FHIR).
Experience working in matrix-based environment with multiple partner groups identifying priorities.
Experience of working in the Pharmaceutical industry and experience of at least one of the R&D lifecycle/functions, e.g. Target Identification and Validation, Lead Compound Generation and Optimization, Chemistry Manufacturing & Controls (CMC), Precision Medicine and Bio Sample Management, Pre-Clinical Development, Cell and Gene Therapy, Clinical Development, Regulatory Affairs, Patient Safety, R&D Quality, Real World Evidence, R&D Project and Portfolio Management.
Experience establishing semantic modelling communities of practice, reusable patterns and training that embed semantic skills across multidisciplinary data, architecture, engineering and AI teams.
Previous experience in ontology modelling and a good understanding of relevant external standards, including CDISC SDTM, HL7 FHIR and related pharmaceutical and healthcare data standards, would be considered an advantage.
When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world!
Why AstraZeneca
Here you will develop the information backbone of a company transforming how science becomes medicine. You will work at pace across business and technology, connecting emerging trends with real-world needs, and simplifying how teams work so that data can be confidently used by analytics and AI. We bring a diverse set of minds together to solve hard problems, value kindness alongside ambition, and give you the space to question, challenge and improve how things are done. Your blueprints and standards will become the platform others build on, turning complex data into connected knowledge that helps our scientists and clinicians deliver life-changing therapies faster!
If you are ready to build the semantic backbone of R&D from Barcelona, send your CV and help turn complex data into connected knowledge that speeds better medicines to patient!
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
