Senior Data Architect
New
C
Cara CareDigital Health
GermanyFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English, German
- Required Skills
- PostgreSQLPythonSQLCloud ComputingSnowflakeData modelingBigQuery
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, or a related field
- Several years of experience as a Data Engineer, Data Architect, or similar role
- Strong expertise in SQL, Python, and modern Cloud Data Warehouses (BigQuery, Snowflake, Postgres)
- Proven experience in data modelling and managing data-intensive systems (Star/Snowflake, ideally dbt experience)
- Hands-on experience with data visualization tools (e.g., Power BI, Metabase, Looker)
- Strong understanding of data modeling, data quality, and data governance principles
- Experience with cloud platforms (AWS, Azure, or Google Cloud)
- Experience with integrating AI Tools in the Daily Workflow (Claude Code, Cursor, Copilot)
- Strong analytical mindset with excellent problem-solving skills
- Excellent communication and collaboration abilities
- Fluent in English; German is a strong plus
Responsibilities
- Own the design and evolution of our end-to-end data architecture, ensuring scalability, reliability, and long-term maintainability
- Define and implement data modeling standards, including domain models, schemas, and data contracts across the organization
- Architect and optimize modern data pipelines (batch and real-time), ensuring robust and high-quality data flow across systems
- Build and evolve our core data platform (warehouse/lakehouse), balancing performance, cost, scalability, and simplicity
- Establish strong data quality, observability, and governance practices to ensure trusted and consistent data across the company
- Translate complex business requirements into scalable and well-structured data architectures
- Collaborate closely with Product, Engineering, and Leadership to align data architecture with product and business strategy
- Enable self-serve analytics by designing clean, well-structured datasets and semantic layers
- Leverage automation and AI where it meaningfully improves data engineering efficiency, reliability, or insight generation
- Continuously evaluate and introduce modern data technologies, patterns, and best practices
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