Designs and delivers the data and AI architecture that powers next-generation analyticsDeep technical expertise with strategic influence to build a scalable, intelligent data ecosystem for the organization. Company based in GenevaDescriptionData Architecture & StrategyDevelop and maintain enterprise-level data models and architectural blueprintsDefine standards for scalable, interoperable, and future-proof data architectureRecommend technologies for semantic search, knowledge representation, and AI-driven data servicesAI & Data Intelligence EnablementAssess and integrate advanced data technologies (vector stores, graph databases, embeddings, retrieval pipelines) Contribute to the design of AI-enabled products such as knowledge assistants and document intelligence toolsPromote practices that optimize data assets for machine learning and LLM applicationsCollaboration & ImplementationServe as the main point of contact for data initiatives (Data Hub, Self-Service Analytics, RAG-based applications) Support the evolution of the Data Warehouse and Data Hub to align with business prioritiesCollaborate with development teams to ensure alignment with architectural standardsGovernance & ComplianceDefine and enforce governance frameworks including glossary, cataloging, and metadata managementEnsure data consistency, lineage tracking, and proper documentation across platformsWork with security and legal stakeholders to maintain compliance with regulatory
Continuous ImprovementStay informed on emerging technologies in data and AILead initiatives to improve data quality, accessibility, and readiness for AI workloadsProfileStrong background in data architecture or data engineering, with exposure to AI-focused systemsHands-on experience with cloud-based data platforms (AWS, Oracle Cloud Infrastructure) Knowledge of vector databases (e. , Pinecone, FAISS) and graph databases (e. , Neo4j, AWS Neptune) Familiarity with data governance frameworks (e. , DAMA-DMBOK) and metadata standardsUnderstanding of ML pipelines and LLM integrations (RAG, semantic search) Proficiency in SQL, Python, and modern ETL/ELT approachesAwareness of emerging AI regulations (e. , EU AI Act) Strong leadership, communication, and project coordination skillsAbility to translate complex technical concepts into clear business valueCurious, strategic mindset with a strong drive toward innovationJob OfferInternational environmentAttractive package.
REQUIREMENTS
Continuous ImprovementStay informed on emerging technologies in data and AILead initiatives to improve data quality, accessibility, and readiness for AI workloadsProfileStrong background in data architecture or data engineering, with exposure to AI-focused systemsHands-on experience with cloud-based data platforms (AWS, Oracle Cloud Infrastructure) Knowledge of vector databases (e. , Pinecone, FAISS) and graph databases (e. , Neo4j, AWS Neptune) Familiarity with data governance frameworks (e. , DAMA-DMBOK) and metadata standardsUnderstanding of ML pipelines and LLM integrations (RAG, semantic search) Proficiency in SQL, Python, and modern ETL/ELT approachesAwareness of emerging AI regulations (e. , EU AI Act) Strong leadership, communication, and project coordination skillsAbility to translate complex technical concepts into clear business valueCurious, strategic mindset with a strong drive toward innovationJob OfferInternational environmentAttractive package.