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Data Architect
Job in
Wembley, Greater London, HA0, England, UK
Listed on 2026-06-20
Listing for:
Experis
Full Time
position Listed on 2026-06-20
Job specializations:
-
IT/Tech
Data Engineering, Data Warehousing, Data Security
Job Description & How to Apply Below
- Data Modelling Standards: Establish the enterprise Data Vault, defining star schema patterns, Snowflake object hierarchies, and modelling conventions to 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 via Snowflake, ensuring data integrity, lineage tracking, and end-to-end auditability.
- Snowflake Governance: Own the full security model for the Snowflake platform, including RBAC policy design, dynamic data masking, row-level security, and comprehensive audit logging across all environments.
- Fin Ops for Data: Monitor Snowflake credit consumption, identify and remediate costly query anti-patterns, and implement warehouse scheduling strategies to optimise data platform spend.
- AI Readiness: Architect data stores specifically designed for LLM consumption, including vector databases, embedding pipelines, and RAG-compatible data structures that underpin the organisation’s AI product layer.
- Data Contracts: Collaborate with Business Analysts to define and document formal data contracts between systems, creating clear, agreed interfaces between data producers and consumers across the platform.
- Experience: 10+ years in Data Engineering or Data Architecture, including at least 4 years specialising in Snowflake platform design and governance.
- Data Architecture Expertise: Deep knowledge of data warehouse design methodologies (Inmon, Kimball, Data Vault 2.0), with the ability to apply the most appropriate approach for each use case.
- Technical
Skills:
Advanced SQL and Python skills, with hands-on experience using dbt (data build tool) or similar transformation frameworks. - AWS Integration: Strong understanding of AWS IAM, S3-based data lake architectures, and Private Link for secure cross-cloud connectivity.
- AI Capability: Practical experience designing data infrastructure for AI/ML workloads, including vector databases, embedding storage, and RAG pipeline integration.
- Soft Skills: Excellent communication and stakeholder management skills, with the ability to translate complex data architecture concepts into clear, accessible language for both technical and non-technical audiences.
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