Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 7 years of experience in data architecture, data engineering, or a related field.
  
• 5 years of experience working with data platforms and technologies.
• Experience designing and implementing data solutions for enterprise customers with hybrid and multi-cloud in the financial markets industry.
• Experience with data modeling and knowledge graph (e.g., RDF, property graphs, graph databases).
• Ability to communicate in English and German fluently in order to support client relationship management in Germany.

Preferred qualifications:

• Relevant Google Cloud certifications (e.g., Professional Data Engineer, Professional Cloud Architect).
• Experience designing and implementing data governance, security, and compliance in a highly regulated environment.
• Experience contributing to open-source data projects.
• Familiarity with machine learning and AI concepts and their application in data management and analysis.
• Knowledge of emerging agentic technologies and their implications for data architecture.

About the job

The role of the Global Solutions & Consumer AI team is to accelerate customer value and increase adoption by delivering innovative, repeatable, and enterprise-ready solutions focused on business value. Make Google Cloud the preferred choice for customers by delivering the highest-value, industry relevant solutions.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Act as a primary technical point of contact for a large, complex financial services customer, building deep relationships with key stakeholders (CTOs, CDOs, Data Architects).
• Design and implement robust data governance frameworks, including data lineage, metadata management, access controls, and compliance with industry regulations (e.g., GDPR, CCPA, BCBS 239).
• Develop and advocate for strategies for federating data responsibility across different business units and technical teams, enabling decentralized data ownership while maintaining centralized oversight and consistency.
• Design and implement data capabilities that seamlessly span across on-premise environments and multiple cloud providers, ensuring data portability, interoperability, and consistent access.
• Identify and address data requirements for emerging agentic technologies, such as real-time data streaming, vector databases, and secure data sharing for autonomous agents.