Enterprise Architect - Data & AI Capabilities Lead

Grade: Employee

You will be the lead of our Data and AI capabilities’ squad, in charge of managing the architecture for data, integration, analytics and AI. You will manage a team of approximately 3 architects, mentor and empower them to own and provide the architectural services for the Data and AI capabilities. You will also act as the Product Owner for the squad ensuring that the backlog is transparent, managed, aligned with stakeholders’ expectations, and delivered.

Together with your team, you will drive, deliver and contribute to the following services and activities:

Strategy & Architecture

• Own the Data and AI target architecture and roadmap, translating business priorities into capabilities and investment choices;

• Define principles, reference architectures, reusable patterns, architecture standards and exception processes across data platforms, operational data stores, master and reference data, integration, data products, analytics and AI;

• Manage the Data Design Authority, ensuring consistent decisions, standards compliance, transparent exceptions and timely escalation of material risks.

• Actively contribute to the architecture community through coaching and mentoring of architects and driving structural improvements to the architecture practice.

Transformation, Delivery & FinOps

• Guide Transition Architectures for scalable, secure, resilient and cost-efficient solutions across operational data stores, analytical platforms and AI use cases;

• Define the role and boundaries of operational data stores, ensuring governed near-real-time data, alignment with systems of record, reconciliation, lineage, quality and lifecycle controls;

• Govern data ingestion, APIs, events and change data capture to improve interoperability, control replication and manage data and technical debt;

• Embed FinOps through cost attribution, forecasting, usage optimisation, showback/chargeback and business-value measurement.

Data Management, Governance & Compliance

• Define Architecture Standards for data modelling, metadata, lineage, quality, authoritative sources, reconciliation, retention and lifecycle management;

• Define the architecture and governance for master and reference data, including ownership, common identifiers, golden records, hierarchies, matching, survivorship, versioning and synchronisation across systems;

• Establish ownership, stewardship, decision rights, policies and controls across business, technology, risk, security and compliance;

• Translate data, privacy and resilience obligations into auditable architecture controls, including sovereignty, access, incident response and third-party oversight.

Data Products & AI Enablement

• Define guardrails for reusable data products, semantic models, service levels and data contracts;

• Establish secure patterns for machine learning, generative AI, retrieval-augmented generation and agentic solutions, supported by MLOps and LLMOps;

• Operationalise AI governance through inventory, risk classification, approval, monitoring, issue management and retirement;

• Embed AI security by design, including threat modelling, prompt-injection and data-leakage controls, least-privilege access, secrets protection, human oversight and continuous monitoring.

Stakeholder Management

• Advise senior business, data, risk and technology leaders on architecture choices, trade-offs, value and risk;

• Collaborate and co-create with Data Management Office (DMO), Data & AI Division and AI Center of Excellence.

The profile we are looking for:

Experience

• 10–15+ years in enterprise, data, analytics, AI or platform architecture;

• Proven leadership of Data and AI architecture and transformation in a regulated environment;

• Experience embedding data governance, regulatory controls, AI security and FinOps into architecture and delivery.

• Experience with Financial services, financial market infrastructure or another highly regulated industry is considered as a plus.

Expertise

• Enterprise data platforms, operational data stores, lakehouse, data mesh, cloud and integration patterns;

• Data management and governance, including modelling, metadata, lineage, quality, master/reference data, data products, ownership and stewardship;

• Machine learning, generative and agentic AI, MLOps/LLMOps, responsible AI and AI security;

• Privacy, resilience, regulatory compliance and FinOps for cloud, data and AI;

• Architecture principles, reference architectures, standards, controls and operating models;

• Certified in an Enterprise Architecture framework (e.g. TOGAF), Cloud, Data Management or AI Governance is considered as an advantage.

Skills

• Strategic and systems thinking with pragmatic decision-making;

• Influencing senior stakeholders;

• Excellent communicator, able to adapt messages and style to different audiences and seniority;

• Fluency in English;

• Strong leadership & coaching skills;

• Cross-functional collaboration and engagement.

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