Data & AI Platform Lead at Richemont in Tokyo

Job description

Richemont, one of the world leaders in the luxury sector, has various Houses specializing in jewelry, watches and high-end accessories. Each Maison proudly embodies a tradition of style, quality and craftsmanship and Richemont strives to preserve the heritage and identity specific to each of them. At the same time, we are committed to innovating and designing new products in line with the values ​​of our Houses, through a process of permanent creativity. MAIN PURPOSE This position leads the technical platform and engineering domains for Data & AI in Japan, translating business needs into reliable, secure, reusable and scalable data and AI solutions and services in alignment with global and regional platforms, architecture, governance and standards. The role drives end-to-end execution across multiple technical teams, global and regional teams, and external partners, from the clarification of business requirements and technical feasibility through architecture, design, engineering, integration, testing, deployment, operational acceptance and continuous improvement. The role also holds Level 2 technical accountability for assigned Data & AI platforms and services, converting insights from day-to-day operations and incidents into continuous improvements in data quality, reliability, automation and reusable technical capabilities. KEY RESPONSIBILITIES 1. Data & AI Platform and Technical Leadership Lead the technical execution of Data & AI initiatives in Japan in alignment with global and regional Data & AI platforms, architecture, governance and technical standards. Translate business requirements into technical requirements and feasible solution designs, and drive the transition from prototypes or proofs of concept to production services where appropriate. Lead technical architecture and design across data models, data pipelines, transformation logic, semantic layers, system integration and AI solutions, ensuring consistency, scalability, maintainability and reuse. Maximise the use of global and regional platforms, components, data assets and standard patterns, identify gaps against Japan-specific requirements, and convert them into practical technical solutions. Work closely with technical teams and external partners at an operational level, contributing directly to technical design and validation when needed to assure solution quality and feasibility. 2. End-to-End Engineering and Delivery Lead end-to-end execution of data, analytics and AI initiatives from technical feasibility and solution design through build, integration, testing, deployment, technical handover and production readiness. Design and drive data integration and data flows across customer relationship management, marketing, business applications, cloud data platforms, and business intelligence and analytics environments. Manage dependencies, technical risks, delivery quality and priorities across multiple technical teams, global and regional teams, and external partners, coordinating resources as required. Technically assure the consistency and reliability of key performance indicators, data definitions, data models and semantic definitions for data products and analytics solutions. Promote reusable components, data assets, common patterns and automation beyond individual developments to improve solution scalability and sustainability. 3. Data Governance, Quality and Trust Embed data quality, access control, security, privacy, data ownership and necessary technical controls into solutions in line with global and regional policies and standards. Work with business stakeholders and data owners to clarify key data definitions, ownership, dependencies, quality expectations and validation methods. Drive the development of trusted data foundations and reusable data assets for shared use across data, analytics and AI. Identify root causes of data quality issues, technical incidents and recurring problems from data, technology and integration perspectives, and convert f