Beschreibung
TASKS WITH IMPACT
• You further develop the data architecture of our central Data Platform and define robust target pictures for Data Vault, data products, business marts and reporting
• Business requirements are translated by you into sustainable data models and architecture decisions along a clear layer architecture, from the landing zone through the Raw Vault and Business Vault to consumable data products
• Modeling standards and methodological guardrails for Data Vault, Kimball-oriented consumption layers and the structured transition from legacy to target architectures are in your hands
• Definition of domain-oriented data products that are consistent, reusable, aligned with the role and access concept, and technically clean for internal and external use
• Together with data engineers, analytics engineers, platform teams and business departments, you turn architecture decisions into actionable guidelines for dbt, Snowflake, Prefect and continuous integration and continuous delivery processes
• Governance topics such as data quality, test strategies, auditability, security, data masking, anonymization and role-based access concepts are actively driven forward by you
• Technical standards for documentation, testing, deployment and architecture transparency are established by you so that they take effect in day-to-day engineering
YOUR SKILLS
• Several years of experience as a Data Architect, Lead Data Engineer or in a comparable role in the modern cloud and data warehouse environment
• Very good understanding of modern data architectures, particularly in the interplay of Snowflake, dbt, orchestration and consumption-oriented data products
• Well-founded experience in data modeling using Data Vault and Kimball
• Experience in building or further developing scalable platform architectures with clear layers, responsibilities and governance principles
• Very good knowledge of SQL and a solid technical understanding of data integration, automation and orchestration, ideally with Python and Prefect
• Experience with architecture governance, data quality, test strategies, observability and security concepts such as RBAC, dynamic data masking or anonymization
• Routine in version-based development processes with Git, pull requests, code reviews as well as CI/CD and release processes
• Strong communication skills, conceptual clarity and the ability to align business and technical stakeholders on shared architecture principles
• Very good German and English, spoken and written
WHAT YOU CAN EXPECT
• Great technical scope for shaping a modern data platform landscape with a clear target architecture.
• An environment in which architecture is not only documented, but consistently implemented in standards, pipelines and data products.
• Close collaboration in an international, cross-functional environment made up of architecture, engineering and business departments.
• A lot of personal responsibility, fast decision-making and the opportunity to further develop the data landscape with visible impact.
