Reference code: JR135400
Richemont vereint einige der weltweit führenden Maisons für Luxusartikel mit besonderer Expertise in der Fertigung von Schmuck, exklusiven Uhren und Premium-Accessoires. Jedes Maison steht für eine eigene stolze Tradition des Stils, der Qualität und des Handwerks, und Richemont hat sich zur Aufgabe gemacht, das einzigartige Erbe und die Identität jedes einzelnen Maison zu bewahren. Gleichzeitig verpflichten wir uns in einem kontinuierlichen kreativen Schöpfungsprozess der Innovation sowie der Entwicklung neuer Produkte auf Grundlage der besonderen Werte unserer Maisons.
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 findings into sustainable improvements.
- Ensure technical documentation, quality assurance, knowledge transfer and operational readiness so that new and changed solutions transition into stable operations.
4. Level 2 Technical Service Ownership and Continuous Improvement
- Be accountable for Level 2 technical issue resolution, service quality, escalation and operational acceptance for assigned Data & AI platforms and services.
- Coordinate internal teams, global and regional teams, and external partners to resolve technical issues involving data pipelines, data models, integrations, analytics platforms and AI solutions.
- Maintain internal accountability for the assigned technical scope even when execution is performed by external partners or other teams.
- Analyse incidents, recurring issues and service data, and drive improvements in reliability, data quality, monitoring, automation, documentation and support processes.
- Confirm that technical ownership, support models, monitoring, documentation, knowledge transfer and escalation paths are appropriately established before new or changed solutions move into production.
5. Cross-team Execution and Capability Development
- Structure execution plans, priorities, dependencies and resource requirements across business, project and technical teams, global and regional teams, and external partners, and drive delivery.
- Make technical risks, capacity constraints, dependencies and decision points visible, address them within the delegated scope, and present options and recommendations to senior decision-makers when required.
- Promote knowledge sharing and coaching in data engineering, analytics engineering, AI and architecture, contributing to stronger technical capability across the organisation.
- Continuously monitor developments in data and AI technologies and engineering practices, and recommend practical opportunities that fit global and regional standards and Japan business needs.
- Lead execution within the assigned scope while providing analysis and recommendations, and escalating Japan-wide Data & AI strategy, final investment decisions and budget decisions beyond delegated authority to senior decision-makers.
COMPETENCIES AND EXPERIENCES
- Substantial professional experience in data, analytics, data engineering, AI, solution engineering or a related field.
- Experience leading data, analytics or AI solutions from requirements and feasibility through design, build, integration, testing, deployment and production operations.
- Practical knowledge and experience in data modelling, SQL, data pipelines, data transformation and modern data architecture.
- Experience delivering solutions using cloud data platforms, data warehouses or lakehouses, transformation tools, business intelligence or semantic layers, or equivalent technologies.
- Ability to translate business requirements into technical requirements and scalable solutions.
- Ability to drive complex delivery across technical teams, global and regional teams, and external partners.
- Ability to identify root causes of technical incidents, data quality issues and recurring problems and convert them into sustainable improvements.
- Practical understanding of data governance, data quality, access management, security and privacy.
- Technical depth and leadership to engage deeply in technical discussions while leading delivery by engineers and partners.
- Ability to structure complex technical risks, dependencies and decision points and explain them clearly to business and technical stakeholders.
- Business-level English and native-level Japanese.
- Bachelor’s degree or equivalent professional experience.
- Experience with BigQuery, dbt, Looker or equivalent cloud data and analytics technologies.
- Experience implementing and productionising generative AI, AI agents, machine learning or enterprise AI solutions.
- Experience integrating data across CRM, marketing, customer data, retail or enterprise applications.
- Knowledge and experience in engineering practices such as DevOps, DataOps, MLOps, continuous integration and continuous delivery, monitoring and automation.
- Experience delivering within global platforms or federated operating models while balancing global standards and local requirements.
