Regional Head of Sub Vertical Consumer Retail & Fashion

The Data Architecture & Management Team is a diverse team which is located across Europe but mainly in Germany. The team is defining the Global Data Architecture guidelines and principles and is responsible for the Data Management topics including Data Quality.

Meaningful & Challenging - Your Tasks

• Business Information Model: Closely work with other Architects in developing Business Information Model, which will serve as a crucial blueprint.
• Architecture Modelling: Develop and maintain architecture and business process modelling that align with business requirements, ensuring data accuracy, consistency, and availability for various data related use cases.
• Data Governance: Collaborate with cross-functional teams to help establish data ownership and stewardship. Collaborate with multiple business units to develop a common data domain model.
• Data Catalogue: Integrate architecture best practices in common business vocabulary that enables all stakeholders to understand and communicate about data assets, including their structure, usage, and governance requirements.
• Data Storage: Evaluate and select appropriate data storage solutions considering scalability, performance, and cost-effectiveness.
• Data Monetization: Identify revenue generation opportunities from data assets and collaborate with stakeholders to create data products and services.

Authentic & Ambitious - Your Profile

• Bachelor’s or Master’s degree in relevant fields with multiple years of experience in IT and data architecture & management topics.
• Proven experience in working with stakeholders from different countries who have different technical and business backgrounds.
• Hands-on working experience with architecture & business process modelling, as well as implementing data architecture principles in multiple projects.
• Good knowledge of Data Lake, Data Warehouse, Data Integration concepts as well as the latest Azure data technologies and trends, data monetization strategies and successful implementation.
• Strong proficiency in data Catalogue tools like Erwin-DIS, Collibra, Informatica.
• Familiarity with AI and ML frameworks, algorithms, and integration into data architecture.
• Language proficiency in English.