Lead Data Architect

Context

The Lead Data Architect will be responsible for designing, implementing, and governing the data and AI architecture that supports all strategic initiatives within the organization. This role focuses on creating seamless data strategies across various functions, including Center of Expertise (COE) steering, design authority coordination, architectural standards definition, integration platforms, and business unit adoption.

Missions

• Design support & governance: Oversee and support the Group DATA/AI Design Authority by reviewing program designs, identifying critical dependencies, establishing key structural choices, and defining clear escalation processes (Local ADR, Global ADR, ARB).

• Center of Expertise (COE) steering: Guide and drive the DATA/AI COE to ensure alignment between technical execution and overall business strategy.

• Standards & blueprints definition: Establish and maintain comprehensive DATA/AI/Integration architecture blueprints covering core components, technologies, and best practices.

• Service consolidation: Collaborate closely with DATA/AI and Operations management to define and consolidate the global service framework, including SLAs and stakeholder responsibilities.

• Integration platform management: Manage and deploy the Kafka streaming integration platform, overseeing contracts, partnerships, support models, and technical blueprints.

• Cross-functional collaboration & adoption: Support Business Units, Business Lines, and Platforms in adopting DATA/AI standards. Help teams understand strategic impacts, organizational shifts, and skill requirements, while routing support to the appropriate actors (Group CDO, DATA teams, or Operations).

• Multi-continental cloud management: Design and optimize cross-continental data operating models, specifically addressing challenges related to data gravity, data performance, and data replication/reconciliation.

• Innovative data solutions: Leverage technical expertise in Databricks (storage/processing) and Kafka (streaming) while applying strong foundational data governance to bridge technical capabilities with business and organizational impact.