Enterprise Architect – Data, AI & Platforms

About the role

The Enterprise Architect – Data, AI & Platforms is a senior leader responsible for shaping the end‑to‑end technology architecture that powers data‑driven decision making, intelligent automation, and digital transformation across the enterprise.

Key responsibilities

• Define and maintain target architecture for data, AI, platforms, and infrastructure, including principles, standards, patterns, and reference models.
• Establish architecture guardrails that ensure data quality, AI readiness, platform automation, integration, standardisation, and security by design.
• Lead design of data lakes, data warehouses, real‑time streaming, ML platforms and model lifecycle management.
• Drive cloud, hybrid and on‑prem modernisation to support advanced analytics, AI workloads and scalable digital platforms.
• Architect solutions for large‑scale analytics, ML training/inference, model deployment and intelligent automation.
• Oversee platform lifecycles including virtualization, container platforms (OpenShift/Kubernetes), cloud services, storage and enterprise databases.
• Guide selection and implementation of infrastructure and cloud technologies that enable AI acceleration and distributed computing.
• Partner with business and technology stakeholders to translate outcomes into scalable, future‑proof architectures.
• Provide technical leadership, mentorship and conduct cross‑functional architecture reviews.

Required profile

• Senior‑level experience leading enterprise architecture initiatives in data, AI and platform domains.
• Proven ability to design and govern complex, multi‑cloud and hybrid environments.
• Strong understanding of regulatory compliance for data governance, security, privacy and residency.
• Excellent communication skills to influence both technical and business audiences.

Required skills

• Data lakes and data warehouses
• Real‑time streaming platforms
• Machine learning platforms and model lifecycle management
• Cloud, hybrid and on‑prem infrastructure
• Virtualisation and container platforms (OpenShift, Kubernetes)
• Enterprise databases and storage modernization
• AI acceleration and distributed computing technologies
• DevOps, MLOps and DataOps concepts