Data Architect

Responsibilities

• 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.
• 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

• Partner with Business Analysts and domain teams to define and document formal data contracts between systems, establishing clear, agreed interfaces between data producers and consumers across the platform.

The Ideal Candidate

Experience

• 10+ years in Data Engineering or Data Architecture, with a minimum of 4 years specialising in Snowflake platform design, optimisation, and governance.
• 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.
• 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.

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