Senior Data Modeler

• Design and implement a robust Hub-and-Spoke data model (e.g., Data Vault 2.0 or specialized Star Schema) to decouple core business entities from source-specific attributes
• Establish "Golden Records" within the Hub to ensure a single version of truth for master data (Customer, Product, Asset)
• Standardize "Spoke" delivery layers to provide high-performance, domain-specific data marts for downstream AI/ML consumption
• Design data structures for event-based, streaming integration patterns including structured and unstructured data
• Develop and maintain the Semantic Layer to abstract complex SQL logic into business-friendly terms
• Map data relationships into an Ontology/Knowledge Graph format to support RAG (Retrieval-Augmented Generation) and AI reasoning
• Define the logic for metrics, attributes, and hierarchies once, ensuring consistency across all AI agents and BI tools
• Integrate data models with an Enterprise Data Catalog (e.g., Atlan, Purview)
• Automate metadata harvesting to ensure the catalog reflects real-time lineage, quality scores, and ownership
• Tag data with semantic descriptors to enable AI-driven "natural language to SQL" capabilities
• Structure data to support feature stores and vector databases
• Collaborate with Data Engineers to ensure "Data-as-a-Product" delivery standards
• Enforce strict interoperable data modeling standards that prioritize data quality, privacy, and auditability
• Lead Community of Practice for Data Modelling and drive continuous improvement

• BSc or MSc in relevant fields such as Computer Science, Information Science, Data Science, Artificial Intelligence, Knowledge Engineering, Computational Linguistics, or related discipline
• 2+ experience in Data Architecture, Semantic Architecture, Knowledge Architecture, Data Integration and Metadata Management
• Data Vault 2.0, Dimensional Modeling (Kimball), 3NF, Strong understanding of one or more of the following: Knowledge Models, knowledge graphs, metadata storage, versioning, tagging and modelling
• Experience with automated metadata management and lineage tools
• Advanced SQL, Python (for metadata automation) / PySpark, and SPARQL/Cypher
• Hands-on experience designing data architecture patterns to support RAG pipelines, prompt / grounding patterns, context orchestration and memory management
• Understanding of how structured data feeds into Vector DBs and LLM contexts
• Systems Thinking: Ability to see the "Big Picture" and how one change impacts the entire network
• Acting as the bridge between the "Data Producers" (Engineers) and "Data Consumers" (Data Scientists)
• Experience with relevant technologies such as graph databases, vector / search platforms, semantic web technologies, event-based streaming platforms, and cloud AI / data ecosystems
• Exposure to tools such as Databricks, Snowflake, Fabric, DBT, Looker, AtScale, Cube, Lakehouse, DeltaLake, Neo4j, Stardog, Ontotext GraphDB, Azure ecosystem, Kafka or equivalent streaming technology
• Experience working with enterprise knowledge and data domains such as PLM, PIM / MDM, LIMS, Regulatory, DAM, ERP, SCM, or other structured and unstructured business data sources
• Strong influencing, interpersonal, and communication skills, with the ability to work effectively across Data, AI, Technology, and business functions
• Experience presenting solutions, trade-offs, and recommendations to senior management and technical leadership teams

A definite advantage would be a passion for naming conventions and definitions—if the AI doesn't understand the column name, neither will the user

• Coaching and mentoring programs
• Development opportunities
• Equal opportunity employer
• IT Equipment
• Work with iconic brands
• Supportive team