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# Sr Specialist Data Architect

- **Company:** Nespresso Deutschland GmbH
- **Location:** Frankfurt am Main, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Cloud Computing, Data Architecture, Data Governance, Data Structures, Data Systems, Data Vault Modeling, Graph Database, Python (Programming Language), SQL Databases, Enterprise Data Management, Large Language Models, Physical Data Models, Databricks
- **Published:** August 29, 2026
- **Apply:** https://www.adzuna.de/details/5858242709

## About the Role

* Degree in relevant field and 7+ years of experience in data architecture.
 * Strong expertise in data modeling and cloud platforms (Azure preferred).
 * Experience with SQL, Python, Databricks, and AI/ML data design.
 * Strong communication and stakeholder management skills.

Nice to Have:

 * Knowledge of Data Vault, vector databases, and GenAI architecture.
 * Experience with graph databases and dbt tools.

## Description

The Sr Specialist Enterprise Data Architect is responsible for enabling data-driven decision-making, analytics, and AI capabilities across Nestlé USA. This role focuses ondesigning, standardizing, and governing enterprise data models that are reusable, scalable, secure, and AI-ready. You will partner with business and technical teams to deliver high-quality data assets and support the evolution of future data and AI architecture., * Define and maintain enterprise data models, hierarchies, and standards.

 * Design conceptual, logical, and physical data models.
 * Own canonical data models and ensure harmonization across initiatives.
 * Provide architecture governance and documentation

Advanced s AI-Ready Data Architecture

 * Design data architecture for Advanced Analytics and GenAI use cases.
 * Enable LLM, RAG, and vector-based data solutions.
 * Support knowledge graphs and advanced modeling techniques.

Metrics, Hierarchies s Business Alignment

 * Define KPIs, metrics, and standardized business definitions.
 * Align data structures with business rules and analytical needs.
 * Ensure consistency and quality across domains.

Data Governance, Quality s Enablement

 * Ensure metadata, lineage, and governance standards.
 * Implement data contracts and quality controls.
 * Support security, privacy, and compliance requirements


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