The Enterprise Data Architect will define and evolve the enterprise data architecture to support the firm’s business strategy, technology roadmap and product/platform operating model, ensuring data is treated as a strategic asset and is fit for analytics, reporting, operational decisioning and AI use cases.
A key part of the role is shaping and embedding a data product operating model (clear ownership, lifecycle management and governance of data products) in support of the wider product and platform model, in collaboration with the Data & AI team.
Main duties and responsibilities
The role holder will line manage two Data Architects and will work alongside Business Architects, Enterprise Architects and Enterprise Integration Architects to shape the end-to-end target state. Working with colleagues across Data, AI, Engineering, Security and Product and Platform teams, the role defines the vision, principles, standards and reference architectures, and guides delivery teams to implement scalable, governed and reusable solutions.
- Develop and communicate the enterprise data architecture vision and strategy, aligned to business priorities and the technology roadmap, in support of the firms broader data & AI strategy.
- Define and embed the target-state data platform and data product architecture (including metadata, lineage and governance) that enables trusted analytics and AI/ML (e.g., feature reuse, RAG-ready knowledge assets).
- Be accountable for the data architecture within the enterprise data platform, ensuring it remains coherent, scalable and aligned to enterprise standards (e.g., data modelling, metadata/lineage, security and governance).
- Define, embed and govern lakehouse patterns such as medallion architecture (bronze/silver/gold), including ingestion, refinement, quality controls and promotion of curated datasets/data products.
- Define, embed and help implement the data product operating model (domain ownership and stewardship, data product lifecycle, data contracts, and data SLAs/SLOs) to enable reuse and accountability across the firm, in collaboration with the Data Governance & Enablement Team.
- Define and embed decision rights and RACI for enterprise data architecture and data products (e.g., ownership, approval and escalation paths across domains, platform teams, Data Governance & Enablement and other stakeholders).
- Create and maintain a high-level roadmap for evolving enterprise data capabilities (data quality, master/reference data, analytics and AI enablement), including transitional architectures.
- Define, embed and govern enterprise standards, patterns and reference architectures (data modelling, data integration, data products, semantic layers and AI data/knowledge patterns) to ensure consistency, interoperability and reuse.
- Engage key stakeholders (Information Security, Office of the General Counsel, and the Data Governance & Enablement Team) to ensure architecture choices meet security, privacy, regulatory and governance requirements.
Boundary with Enterprise Integration Architecture: This role owns the enterprise data architecture (data domains, models, data products, semantics, governance requirements and AI data/knowledge patterns). Enterprise Integration Architecture owns the integration strategy and design for moving data between systems (integration patterns, middleware, APIs/eventing, orchestration and interface standards). The Enterprise Data Architect partners with Enterprise Integration Architecture to ensure integration designs correctly implement the target data architecture, data quality, lineage, security and compliance controls end-to-end.
The role will also partner with delivery teams to turn strategy into outcomes, providing thought leadership, practical guidance and solution direction. Working closely with Data & AI colleagues and fellow architects (Business, Enterprise and Enterprise Integration), the Enterprise Data Architect helps shape investment choices, ensures solutions align to enterprise principles, and promotes responsible, secure and scalable use of data for analytics and AI. The role provides clear guardrails and an architecture runway for teams, favouring early engagement and enablement over late-stage approvals.
- Lead architecture governance and design assurance for data, analytics and AI solutions against enterprise principles, working alongside Business, Enterprise, Integration, Cloud and Solution Architects to align end-to-end design.
- Support platform and product owners with live environment compliance reporting, including data controls that support Responsible AI (e.g., lineage, data quality, access, retention) and model-operational readiness.
- Partner with Product and Platform leadership, in collaboration with the Data & AI team, to embed data product ways of working (prioritisation, backlogs, funding/investment cases, and OKRs/KPIs) and to ensure data products are treated as first-class products.
- Work with Information Security, Office of the General Counsel and the Data Governance & Enablement Team to define and embed controls (access, privacy, retention, usage restrictions, lineage and auditability) required for trusted data and Responsible AI.
- Identify reusable enterprise patterns (data products, canonical models, semantic definitions, feature/embedding reuse, RAG knowledge sources) and promote adoption across domains.
- Promote standardised approaches, technologies and ways of working with Data & AI colleagues within the product/platform model (reference implementations, reusable components, and clear guardrails).
- Reduce enterprise-wide complexity and cost by rationalising data flows, consolidating duplicative datasets, and improving platform and integration efficiency.
- Enable business process improvement and increased productivity by ensuring trusted, well-governed data assets that can safely power automation, analytics and AI-assisted workflows.
- Assist in assessing fitness of data and AI-enabling architectures in live environments against resilience, performance and failure tolerance expectations (including data SLAs/SLOs and dependencies for AI/ML solutions).
About you
- Strong experience in enterprise data architecture (capability modelling, enterprise data models, target-state visioning, roadmaps, principles, standards and governance).
- Experience defining and implementing a data product operating model (product ownership, lifecycle management, data contracts/SLAs, stewardship, and adoption measures) within a wider product/platform operating model.
- Experience establishing data product lifecycle hygiene at scale (e.g., versioning, documentation, backwards compatibility, change management, deprecation/retirement and support).
- Experience establishing and evolving architecture services and artefacts (principles/standards lifecycle, reference architectures, reusable patterns), including driving adoption and measuring outcomes (e.g., reduced duplication, improved data quality, faster delivery).
- Proven experience working in an Enterprise Architecture operating model, collaborating with Business, Enterprise, Integration, Cloud, Solution and Security architecture to resolve cross-domain decisions and ensure coherent end-to-end designs.
- Experience working within an enterprise architecture tool/repository to relate data architecture concerns to other domains (business, application, integration and technology architecture), including traceability of decisions, standards and roadmaps.
- People leadership experience, including line management and coaching of architects to build capability, consistency and delivery effectiveness.
- Strong expertise in data modelling and information design (conceptual, logical and physical), including dimensional modelling and working knowledge of 3NF; able to set modelling standards and provide pragmatic guidance to delivery teams.
- Experience with enterprise data tooling and enablement for modellers and engineers (e.g., modelling repositories, templates/standards, data quality & testing, CI/CD, catalog/lineage integration and developer workflows).
- Demonstrable experience shaping conceptual, logical and physical data architectures, design patterns and best practices for data integration, data products, semantic layers and analytics platforms.
- Experience defining and governing semantic layers and enterprise metrics (common definitions, calculation logic, master KPI sets, and controls to ensure consistency across reporting, analytics and AI use cases).
- Experience defining metadata architecture (catalogue, lineage, glossary) and Master Data Management (MDM) / Reference Data Management (RDM) solutions to improve discoverability, trust and reuse.
- Experience performing analysis and design for data management, analytics and AI-driven initiatives, translating business outcomes into architecture and delivery guidance.
- Practical understanding of data governance, privacy and security-by-design, and how these enable compliant analytics and Responsible AI
- Experience applying data classification and handling requirements in solution and enterprise designs (e.g., sensitivity tiers, access controls, lawful basis, retention, and cross-border transfer considerations) and embedding these into data products and AI-enabled data flows.
- Strong understanding of enterprise data management disciplines: MDM, metadata management, data quality, data governance, stewardship and operating models.
- Experience with cloud data technologies and modern data/AI platform concepts (lakehouse, streaming, orchestration, ML pipelines, feature stores and vector/embedding stores).
- Experience with medallion architecture and approaches for managing unstructured data sets (e.g., document ingestion, chunking and metadata enrichment, content classification, vector/embedding pipelines, retention and access controls).
- Able to review solution designs independently, provide constructive challenge, and partner with engineering teams to drive quality throughout delivery (including data and AI solutions).
- Experience with enterprise systems and SaaS data challenges (e.g., SAP, Salesforce), including data integration, lineage and responsible sharing/consumption.
- Working knowledge of AI/ML and Generative AI patterns and implications for enterprise data (e.g., RAG, prompt grounding, knowledge curation, data quality/lineage, privacy and security considerations).
- Excellent problem solving and analysis skills.
- Excellent communication skills - written and verbal.
- Excellent presentation skills.
- Intervenes if warning signs of problems occur within own area of responsibility.
- Forward thinking. Proactive rather than Reactive.
- Strong leadership and influence skills across Business and Technology organizations.
- Knowledge of Agile methodologies and their impact on design, build and operating processes as pertains to architecture development.
- Experience with modern data platform technologies, e.g., Azure Databricks (Delta Lake), ADLS Gen2, Azure Data Factory, Power BI, and associated security and operations services (Entra ID, Key Vault, monitoring)
- Experience with data governance and engineering enablement tooling (e.g., Microsoft Purview, Databricks Unity Catalog, GitLab CI/CD, MLflow/MLOps and emerging LLMOps patterns)
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