Job Title: Snowflake Data Architect - 5 DAYS A WEEK ON-SITE
Day Rate: £600-£700 per day
Location: Wembley London - 5 days on-site
Our client, a well-established and diversified multinational organisation, is seeking a Snowflake / Data Architect to join their team.
Skills
Solid experience in Data Engineering or Data Architecture, with solid experience in specialising in Snowflake platform design and governance.
Data Architecture: Mastery of Data Warehouse design methodologies - Inmon, Kimball, and Data Vault 2.0 - with the judgement to apply the right pattern for the right use case.
Technical Skills: Expert SQL and Python; hands-on experience with dbt (data build tool) or equivalent transformation frameworks.
AWS Integration: Solid understanding of AWS IAM, S3 data lake patterns, and PrivateLink for cross-cloud data connectivity.
AI Readiness: Practical experience architecting data infrastructure for AI/ML consumption - vector databases, embedding stores, and RAG pipeline integration.
Soft Skills: Strong interpersonal skills; ability to translate complex data architecture into clear language for Business Analysts and non-technical stakeholders. Duties
Data Modelling Standard: defining Star Schema patterns, Snowflake object hierarchies, and modelling conventions that serve as the Group-wide standard for all data products.
Cross-Cloud Orchestration: Design and implement secure, high-throughput data pipelines connecting AWS S3 and Azure APIs through Snowflake - ensuring data integrity, lineage tracking, and end-to-end auditability.
Snowflake Governance: Own the full security model for the Snowflake platform - RBAC policy design, dynamic data masking, row-level security, and comprehensive audit logging across all environments.
FinOps for Data: Monitor Snowflake credit consumption patterns, identify and remediate high-cost query anti-patterns, and implement warehouse scheduling strategies to reduce operational data spend.
AI Readiness: Architect data stores purpose-built for LLM consumption - including vector databases, embedding pipelines, and RAG-compatible data structures that will serve as the foundation for the clients AI product layer.
Data Contracts: Partner with Business Analysts to formally define and document 'Data Contracts' between systems - creating clear, agreed interfaces between producers and consumers across the data platform
