Data architect

Responsibilities

• 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.
• FinOps 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.

The Ideal Candidate

• 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 PrivateLink 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.