United Kingdom

Snowflake / Data Architect - London, Wembley, LondonLondon

Snowflake / Data Architect - London, Wembley, LondonLondon
Description

Job Title: Snowflake / Data Architect

Salary: Paying up to 90,000 per annum

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 a minimum of 4 years 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 Bestway's 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.
Highlights
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