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About the Role

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. The role drives end-to-end execution across multiple teams and partners, ensuring solutions align with global and regional standards. It also holds Level 2 technical accountability for assigned Data & AI platforms and services, driving continuous improvements.

Responsibilities

Lead the technical execution of Data & AI initiatives in Japan, aligning with global and regional platforms, architecture, governance, and technical standards.
Translate business requirements into technical requirements and feasible solution designs, driving the transition from prototypes to production services.
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.
Maximize the use of global and regional platforms, components, and data assets, identifying gaps and converting them into practical technical solutions for Japan-specific requirements.
Work closely with technical teams and external partners at an operational level, contributing to technical design and validation.
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 CRM, marketing, business applications, cloud data platforms, and BI/analytics environments.
Manage dependencies, technical risks, delivery quality, and priorities across multiple teams and partners, coordinating resources.
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 to improve solution scalability and sustainability.
Embed data quality, access control, security, privacy, data ownership, and technical controls into solutions, adhering to global and regional policies.
Work with business stakeholders and data owners to clarify 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 for smooth transitions into stable operations.
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/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.
Analyze incidents, recurring issues, and service data to 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.
Structure execution plans, priorities, dependencies, and resource requirements across business, project, and technical teams, 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.
Promote knowledge sharing and coaching in data engineering, analytics engineering, AI, and architecture, contributing to stronger technical capability.
Continuously monitor developments in data and AI technologies and engineering practices, and recommend practical opportunities aligned with standards and business needs.
Lead execution within the assigned scope, providing analysis and recommendations, and escalating Japan-wide Data & AI strategy, final investment decisions, and budget decisions beyond delegated authority.

Requirements

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.
Experience with BigQuery, dbt, Looker or equivalent cloud data and analytics technologies.
Experience implementing and productionizing 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.

Skills

Data Modelling
SQL
Data Pipelines
Data Transformation
Modern Data Architecture
Cloud Data Platforms
Data Warehouses
Lakehouses
Transformation Tools
Business Intelligence
Semantic Layers
Generative AI
AI Agents
Machine Learning
Enterprise AI
Data Integration
CRM
Marketing Data
Customer Data
Retail Data
Enterprise Applications
DevOps
DataOps
MLOps
Continuous Integration
Continuous Delivery
Monitoring
Automation
BigQuery
dbt
Looker

Location

Japan

Work Type

Full-time

Experience Level

Substantial professional experience

Education Level

Bachelor’s degree or equivalent professional experience

About the Company

Richemont is one of the world leaders in the luxury sector, with various Houses specializing in jewelry, watches, and high-end accessories.
Each Maison embodies a tradition of style, quality, and craftsmanship.
Richemont strives to preserve the heritage and identity specific to each of its Houses.
The company is committed to innovating and designing new products in line with the values of its Houses through permanent creativity.