Santander Bank

Data Transformation and Usability Specialist

Country: Spain

Chief Data & AI Officer – Data & Analytics Division is seeking a Data Transformation and Usability Specialist who will reports to Global Head of Data Transformation and Usability

Role Mission

Drive the creation of reusable, business-oriented data assets, supporting the implementation of the Group’s single data model and the Data as a Product approach. Ensure that data is structured and readily available to support models, business intelligence, and strategic decision-making across all geographies and business units. Contribute to the maturation of a data-driven culture, accelerating time-to-insight and enabling high-impact analytical outcomes.

Key Responsibilities

• Design the Group’s single data model, ensuring scalability, interoperability, and alignment with business needs.
• Define and evolve reference data structures at multiple readiness levels (model-ready, BI-ready, decision-ready), ensuring consistent local implementation under common principles.
• Promote the Data as a Product mindset, contributing to the creation and maintenance of reusable, modular, and value-driven data assets.
• Collaborate with data, business, and platform teams to align functional requirements, data structures, and global platform capabilities.
• Coordinate with Data Management and Governance teams to ensure data traceability, quality, and governance across the full data lifecycle.
• Promote best practices and tools to accelerate the transformation of data into tangible value, including automation, self-service, and data sharing.
• Participate in continuous improvement initiatives, monitoring and adoption of global standards, domain-driven design principles, and data management maturity across the organization..

Profile Requirements

• Over 8 years of professional experience in data engineering, data architecture, data transformation, or data asset management roles.
• Deep understanding of concepts such as Data as a Product, data model design, metadata management, and modern data architecture.
• Practical experience with cloud data platforms (e.g., AWS, Azure, GCP) and technologies such as PySpark or data catalog tools is highly valued.
• Ability to work across technical and business teams, translating functional requirements into structured data solutions.
• Familiarity with data governance frameworks and principles of domain-driven, product-oriented data design and enterprise data lifecycle management.
• University degree in Engineering, Computer Science, Data Science, Mathematics, Physics or related quantitative disciplines.