About the role

The Principal Data Platform Architectacts as the senior technical authority for the organisation’s data platform technology landscape, accountable for defining, governing, and evolving the platforms that underpin Data Foundations, Data Products, analytics, and AI use cases at enterprise scale.

This role owns the strategic and target‑state architectureof the data platform, including cloud data services, processing engines, integration and streaming technologies, and platform‑level capabilities such as security, resilience, scalability, and performance. Working closely with data engineering, data architecture, security, and platform operations teams, the Principal Data Platform Architect ensures that all Data & AI solutions are built on a consistent, secure, and reusable platform architecturealigned to agreed enterprise standards and principles.

Operating at Principal level, the role balances deep technical leadershipwith architectural governance and assurance Itis responsible forevaluating new data technologies, defining reference architectures and patterns, and guiding complex platform‑level decisions and trade‑offs across the organisation. The role plays a critical part in ensuring that platform evolution supports long‑term business value, cost efficiency, and operational sustainability, rather than short‑term project optimisation.

The Principal Data Platform Architect partners closely with Enterprise, Solution andData Architects, ensuring that platform decisions enable domain‑aligned Data Foundations and Value Pool delivery, without driving fragmentation or unnecessary divergence. While Solution Architects focus on end‑to‑end solution design for individual initiatives, this roleremainsfocused on the platform as a product, ensuring coherence, scalability, and reuse across all delivery streams

The role sits within the Data Architecture and Engineering teaminthe Data and AI Office.

Key responsibilities

Data platform strategy & target architecture

• Define, optimise, and govern the specialised technology landscape for the organisation’s Data Platforms.

• Establish andmaintaintarget‑state platform architectures that support scalable analytics, AI, and data product delivery.

Architecture standards, principles & patterns

• Define and govern data platform architecture standards, principles, reference architectures, and design patterns.

• Ensure consistent adoption of approved platform technologies and patternsacross domains and Value Pools.

• Act as a custodian of platform consistency, preventing fragmentation and uncontrolled divergence.

Technology evaluation & platform evolution

• Evaluate new and emerging data technologies, balancing standardisation, innovation, cost, and risk.

• Define transition paths from current‑statetofuture‑stateplatforms, articulating clear architectural options andtrade‑offs

• Contribute to technology investment decisionsand shapinglong‑termplatform roadmaps.

Architectural governance & assurance

• Provide architectural governance and assurancefor data platform designs across major initiatives.

• Ensure that platform designs are secure, performant, resilient, and scalablebefore being adopted by delivery teams.

• Support technology decisions from a position of informed technical authority

Cross‑functionalcollaboration

• Partner closely with data engineering, platform engineering, security, and operations teamsto ensure platform architectures are practical and operable.

• Work with Enterprise, Solutionand DataArchitectsto ensure platform capabilities effectively supportend‑to‑endsolution delivery without owning solution design.

Security, governance & compliance by design

• Embed data security, privacy, governance, and regulatory requirementsinto data platform architectures by design.

• Ensure platform technologies supportappropriate dataclassification, access controls, and auditability

Thought leadership & capability uplift

• Provide thought leadershipin data platform technologies, analytics, data science, AI, and GenAI.

• Promote strong architectural thinking aligned to enterprise principles rather thanproject‑specificoptimisation.

Qualifications and skills

Essential

• Proven experience in a Data Platform Architector equivalent seniortechnicalarchitecture role within a large, complex enterprise environment.

• Demonstratedtrack recorddesigning and governingenterprise‑scaledata platformssupporting analytics, data products, and AI use cases.

• Stronghands‑onexperience with cloud‑baseddata platforms(Azure preferred or equivalent), including data lakes, analytics platforms, and integration services.

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience

• Data Platform Architecture

• Deepexpertisein data platform architecture, spanning ingestion, transformation, storage, processing, analytics, and integration layers.

• Strong understanding of cloud data foundations, including lake,lakehouse, or warehouse style patterns.

• Experience designing platforms that are scalable, performant, resilient, and cost efficient

• Architecture Standards & Governance:

• Proven ability to define, govern, and enforce platform architecture standards, principles, reference architectures, and design patterns.

• Experience providing architectural assuranceacross initiatives, ensuring platform designs align to agreed enterprise standards.

• Ability to prevent platform fragmentation by driving consistent technology adoptionacross domains and Value Pools.

• Security, Governance & Compliance

• Strong knowledge of data security, privacy, governance, and regulatory requirements, and how to embed these into platform architecture by design.

• Experience designing platforms that support data classification, access controls, lineage, and auditability

• Collaboration & Influence

• Experience working closely with data engineering, platform engineering, security, and operations teamsto ensure architectures are practical and operable.

• Strong stakeholder management skills, with the ability to articulate architecturaltrade‑offs, risks, and decisionsclearly.

• Ability to collaborate effectively with Enterprise and Solution Architectswithout owningend‑to‑endsolution design.

Preferred

Advanced Platform & Technology Experience

• Experience with streaming andevent‑drivendata architectures

• Familiarity with modern analytics and AI platforms(e.g. Databricks,Spark‑basedplatforms, streaming services).

• Exposure to domain‑alignedor federated data architecture models(e.g. data mesh–influenced approaches).

Platform Evolution & Transformation

• Experience contributing to enterprise data platform modernisation or transformation programmes

• Ability to define and communicate transition roadmapsfromcurrent‑statetofuture‑stateplatform architectures.

• Experience supporting technology evaluation and investment decisionsat enterprise level.

Ways of Working & Leadership

• Experienceoperatingin Agile andDevOps‑alignedenvironments, supporting delivery from design through to industrialisation.

• Experience mentoring or coaching other architects and senior engineers, raising overall architectural maturity.

• Strong documentation skills for reference architectures, standards, and guidance artefacts

Certifications & Industry Context

• Relevant cloud or architecture certifications(e.g. Azure, TOGAF‑aligned, or equivalent).

• Experience working in highly regulated or strongly governed industries

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