Head of Data Architecture

Salary: Competitive Plus Benefits
Location: London Store Support Centre, London, EC1M 6HA
Contract type: Permanent
Business area: Sainsbury's Tech
Closing date: 03 April 2026
Requisition ID:
We'd all like amazing work to do, and real work-life balance. That's waiting for you at Sainsbury's. Think about the scale it takes to feed the nation. The level of data, transactions and variety involved. Then you'll realise this is a modern software engineering environment, because it has to be. We've made significant investment in the standards and principles that shape how we work. We iterate, learn, experiment and champion ways of working such as Agile, Scrum and XP. So you can look forward to exciting opportunities across everything from AI to reusable tech.

The Head of Architecture (Data) is central to the business-critical transformation to harness data for Analytics and AI at Sainsbury's. Reporting through to a domain engineering director, at the heart of the role is the accountability to define and maintain and assure Data Architecture and Solution Architecture across the data estate.

This includes ownership of the architectures related to the data platforms and shared capabilities necessary to provision use-case appropriate data products, and the end-to-end solution architectures that consume and operationalise these products - spanning analytics, forecasting, Machine Learning and GenAI solutions, as well as current and future data opportunities.

This role also reflects and drives the skills required within the company and the third parties with whom we work to deliver. The role works closely with the wider Enterprise Architecture and Domain Architecture teams to ensure that business, data, application and technology architectures align with enterprise approaches and standards.

The role requires a highly credible colleague, who will collaborate brilliantly up, down and across Sainsbury's Tech, and within the wider business. A proven thought leader, the Head of Architecture will also be a great leader of others, developing, recruiting, and inspiring a team of technical experts, both directly, and through a matrix of domain and senior architects within our Engineering teams. They will be critical in driving the Data Architecture Practice in the analytical estate - be recognised as a thought leader and SME by others, driving up the level of capability within the data engineering teams.

Finally, it is essential that the Head of Architecture (Data) is experienced in working in a world of agile and iterative in-house development at scale, as well as more traditional waterfall programme delivery.

What I am accountable for

• Own the Data Architecture and Solution Architecture strategy for the analytical data estate, ensuring it is aligned to business strategy, product roadmaps, enterprise standards and measurable outcomes.
• Work closely with the Head(s) of Engineering to ensure architecture for data platforms, data products and solution delivery is informed by functional roadmaps and engineering best practice, maximising return on technology investment, driving reuse, reducing technical debt and enabling decommissioning at pace.
• Serve as a key contributor at Sainsbury's Tech Strategic and Data Domain Technical Reviews, ensuring convergence of relevant artefacts at organisational level and that architectural decisions reduce cost, risk and debt whilst increasing value, pace and quality.
• Work in deep partnership with Enterprise Architecture to ensure data products, data platforms, and end-to-end analytics/ML solution architectures fit with enterprise vision, standards, and technology direction.
• Ensure architectural decisions are made and recorded well, utilising governance forums, templates and "minimum viable documentation", including formal HLD reviews and Key Design Decisions that cover both data architecture/model choices and solution architecture choices.
• Own the conventional data architecture for the analytical estate, including conceptual, logical and physical data architecture, cross-domain integration approaches, data domains/bounded contexts, and the mechanisms for consistent and reusable data.
• Define and iterate data modelling standards and guardrails (e.g. 3NF, dimensional modelling, Data Vault) including clear decision criteria for when each approach is appropriate and ensure adoption across internal and partner teams.
• Own and assure integrated data modelling where required, including canonical entities, conformed dimensions, reference data, master data alignment, and shared business definitions that prevent metric drift and reduce duplication.
• Drive semantic and metrics architecture, ensuring consistent KPI definitions, metric calculation logic and semantic models across data products and consumption layers, reducing reconciliation cycles and improving trust in data.
• Assure data product design quality, ensuring products are discoverable, understandable and usable with clear ownership, documentation, lineage, and fit-for-purpose data contracts (schemas, quality expectations, change compatibility where appropriate).
• Ensure platform capabilities enable good modelling and product practices, rather than working against them, and that patterns are consistent, secure-by-design and operable.
• Set and evolve non-functional requirements and standards for the analytical estate (security, availability, performance, resilience, cost, DR), ensuring teams design for operability and supportability from the start.
• Drive the solution architecture, along with Head(s) of Engineering, patterns for ML/GenAI and advanced analytics, ensuring that data requirements, integration patterns, model/feature lifecycle needs, governance and non-functionals are designed in from the outset.
• Work with Data Science, ML Engineering and Data Engineering leadership to ensure ML solutions are underpinned by robust data modelling and data product design, and that architectural decisions support safe, repeatable and compliant operationalisation.
• Lead, recruit and develop the architecture function (via Data Architecture Managers, Senior/Solution Architects and Data Architects), growing technical depth, pragmatic judgement and leadership behaviours.
• Lead brilliantly in a matrix, influencing across empowered teams, domain architects, principal engineers and strategic partners, ensuring shared ownership and pace without sacrificing standards.
• Champion effective use of data as an asset, articulating complexity in simple terms and inspiring colleagues to engineer within agreed patterns and standards.
• Role model technical leadership externally, scanning the environment for innovation, best practice and partnerships, bringing relevant insights back into strategy and execution.
• Build and nurture deep partnerships with strategic partners, ensuring we see the best of them, and they see the best of us, with clear expectations of quality, standards and outcomes.

How I will succeed

• Deliver a step change in the quality, currency and coherence of the data estate, including modelling consistency, semantic alignment, and architectural clarity.
• Build strong collaboration with Enterprise Data Architecture to optimise provision of data from source and reduce friction across the upstream/downstream data lifecycle.
• Communicate with passion, energy, cultural empathy and expertise, such that architects, engineers and partners actively want to contribute to and deliver the architectural vision.
• Demonstrate deep understanding of how business value is created through data and analytics/ML; communicate effectively with stakeholders at all levels as required.
• Partner brilliantly with peers across Sainsbury's Tech and technology partners; role model compelling collaboration.
• Constantly scan the environment for fresh ideas, innovation and partnerships; selectively adopt what will materially improve outcomes.
• Role model excellent collaboration in agile and "bought-in solution" contexts; ensure governance accelerates delivery rather than becoming theatre.
• Apply influence effectively with the Data Domain Architecture Team and supported business divisions, ensuring alignment, prioritisation and clarity of architectural direction.
• Build a high-performing architecture practice with strong engagement and shared learning, raising the capability of engineering teams through visible leadership.

What I need to know

• Extensive experience creating Data Warehousing and Analytical end-to-end data architectures.
• Deep experience developing data modelling best practices including 3NF, dimensional and Data Vault approaches, and applying them pragmatically.
• Experience creating and managing data products to support Machine Learning and GenAI use cases, including shared feature stores where appropriate.
• Expert knowledge in the use of modelling tools across multiple teams to create consistent cross-domain integrated models.
• Experience creating and maintaining semantic models to optimise and simplify data access pathways across multiple data products, including governance of KPIs/metrics to prevent drift.
• Expert understanding of data integration and pipeline architecture, with strong technical knowledge of ELT/ETL tooling and MPP, columnar, cloud-native databases (ideally Snowflake).
• Strong experience defining Solution Architecture patterns for analytics and ML solutions that consume data products, including integration patterns, non-functionals and secure-by-design approaches.
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