Wembley
Responsibilities • Enterprise Data Modelling Standards, • Define and own a group‑wide data modelling standard, including Star Schema patterns, Snowflake object hierarchies, and modelling conventions that underpin all analytical and operational data products., • Cross‑Cloud Data Orchestration, • Design and implement secure, high‑throughput data pipelines across cloud platforms, integrating AWS S3 and Azure‑based APIs through Snowflake. Ensure data integrity, lineage, and end‑to‑end auditability., • Snowflake Security & Governance, • Own the end‑to‑end Snowflake security model, including RBAC design, dynamic data masking, row‑level security, and comprehensive audit logging across development, test, and production environments., • Data FinOps & Cost Optimisation, • Monitor Snowflake credit consumption, identify high‑cost query anti‑patterns, and implement warehouse sizing and scheduling strategies to optimise operational data spend., • AI‑Ready Data Architecture, • Architect data stores optimised for large language model (LLM) consumption, including vector databases, embedding pipelines, and retrieval‑augmented generation (RAG)‑compatible data structures that form the foundation of the AI product layer., • Data Contracts & Platform Interfaces, • Experience, • 10+ years in Data Engineering or Data Architecture, with a minimum of 4 years specialising in Snowflake platform design, optimisation, and governance., • Data Architecture Expertise, • Deep knowledge of enterprise data warehouse design methodologies, including Inmon, Kimball, and Data Vault 2.0, with strong judgement on selecting the appropriate approach for each use case., • Technical Skills, • Expert‑level SQL and Python, with hands‑on experience using dbt (data build tool) or comparable transformation and orchestration frameworks., • Cloud & AWS Integration, • Strong understanding of AWS IAM, S3‑based data lake architectures, and PrivateLink or equivalent patterns for secure cross‑cloud connectivity., • AI & ML Enablement, • Practical experience designing data infrastructure to support AI/ML workloads, including vector stores, embedding pipelines, and integration with RAG‑based systems., • Stakeholder & Communication Skills, • Excellent interpersonal skills, with the ability to explain complex data architecture concepts clearly to Business Analysts and non‑technical stakeholders.