Define and own the end‑to‑end enterprise data platform architecture , spanning:
Snowflake
Databricks (Azure & AWS)
Microsoft Fabric
Supporting cloud and networking services
Establish clear architectural positioning and decision frameworks for when and how each platform is used (e.g. analytics, data products, AI workloads).
Shape the multi‑year data platform roadmap , balancing delivery milestones, risk, cost, and long‑term architectural integrity.
Act as the final architectural authority for data‑platform‑level design decisions.
Data Products & Platform Enablement
Define enterprise standards for data products , including:
Ownership models
Contract‑based interfaces
Reuse, monetisation, and lifecycle management
Enable data democratisation while maintaining strong governance, security, and cost controls.
Establish golden paths, reference architectures, and platform patterns to accelerate safe adoption by application and domain teams.
AI & GenAI on Data Platforms
Define the reference architecture for AI / ML / GenAI solutions running on the data platform estate.
Establish AI solution patterns for:
GenAI applications
AI agents and orchestration
Feature engineering and model serving
Define and own AI SDLC and CI/CD guidance , covering:
Development
Testing
Deployment
Monitoring
Governance
Ensure AI adoption is responsible, cost‑aware, secure, and compliant by design.
Governance, Security & Operating Model
Define and evolve the platform operating model , including:
Clear RACI between platform, application, and security teams
Guardrails vs freedom model
Embed security, compliance, and risk controls into platform architectures and patterns (security‑first by design).
Work closely with:
Security architecture
Risk
Legal and governance forums
Ensure platforms operate in line with enterprise standards, regulatory obligations, and audit expectations .
Cost, Scale & Reliability
Lead platform‑level cost optimisation strategy , including:
Cost attribution
FinOps guardrails
Platform efficiency metrics
Design platforms for scale, reliability, and resilience , leveraging automation and SRE principles.
Reduce operational risk by replacing manual processes with repeatable, automated platform capabilities .
Technical Leadership & Enablement
Act as a hands‑off technical leader while remaining deeply knowledgeable of platform capabilities and limitations.
Mentor senior engineers, architects, and platform teams.
Contribute to Centers of Excellence (CoEs) by defining:
Integration patterns
Architectural standards
Best practices and policies
Represent the organisation externally with strategic partners and vendors where required.
Candidate Profile / Key Skills
Essential
10+ years' experience in data engineering, data architecture, or platform engineering roles.
Proven experience designing enterprise‑scale cloud data platforms .
Strong architectural depth across:
Data warehousing
Data engineering
Analytics platforms
AI / ML workloads
Experience defining platform strategy, roadmaps, and operating models .
Strong understanding of security, governance, and compliance in large regulated environments.
Excellent stakeholder management and communication skills.
Technical Expertise (Breadth over Depth)
Deep expertise in at least one core data platform (Snowflake, Databricks, or Microsoft Fabric), with strong working knowledge of the others, including their architecture and operating characteristics.
Strong knowledge of:
Data product architectures
Lakehouse and warehouse patterns
Streaming and batch processing
Familiarity with AI / ML platforms and tooling (conceptual and architectural level).
Experience with cloud platforms (AWS / Azure) and shared enterprise services.
Expert knowledge of SQL and Python ( or PySpark)
Understanding of DevOps / CI‑CD / Infrastructure as Code from an architectural standpoint.
Desired / Nice to Have
Experience in financial services or other regulated industries.
Experience defining AI governance frameworks or responsible AI controls.
Relevant cloud or platform certifications.
Prior experi
Additional Information
Role Overview
Our cloud data platforms are mission‑critical enterprise capabilities , underpinning analytics, data products, AI solutions, and regulatory workloads across the organisation. We are seeking a Principal Architect to provide technical and architectural leadership across the full data platform estate , including Snowflake, Databricks, Microsoft Fabric .
This role is responsible for defining and evolving the enterprise data platform strategy , establishing architectural standards and patterns , and ensuring data and AI solutions are scalable, secure, cost‑effective, and fit for enterprise use . The role operates at the intersection of platform engineering, architecture, security, and business enablement , with a strong focus on governance by design rather than ad‑hoc control.
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