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# Data Engineer

- **Company:** ANGEHEUERT GmbH
- **Location:** Germany
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Computing, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Document-Oriented Databases, Power BI, SQL Databases, File Transfer Protocol (FTP), Cloud Platform System, Data Classification, Data Layers, Data Lakes, Information Technology, Data Management, Machine Learning Operations, Data Pipelines, Databricks
- **Published:** October 31, 2026
- **Apply:** https://www.adzuna.de/details/5947529584

## About the Role

* Education: A degree in Computer Science, Data Engineering, Business Informatics, or a comparable qualification provides the foundation for this role.
 * Experience: Several years of experience in Data Engineering and building productive data platforms.
 * Technology: Very good knowledge of Databricks, SQL, and modern ETL and ELT patterns.
 * Architecture: Experience with Data Lake or Lakehouse architectures, data modelling, and data quality.
 * Cloud: Experience with Infrastructure as Code in cloud environments is important, ideally complemented by Azure or AWS knowledge.
 * Operations: Experience with monitoring, error handling, and operationalizing productive data pipelines.
 * Communication: Strong communication skills and very good English at C1 level.
 * Additional Skills: Experience with banking, finance, Power BI, DORA, SFTP, or AI and ML data pipelines is an advantage.

Do you have your own ideas about how a modern data platform should be built and want the freedom to put them into practice? Then this role offers the technical freedom you rarely find within established structures.

## Description

* Data Layer: Take ownership of operating and further developing the Databricks based data platform, including workspaces, jobs, and permissions.
 * Data Pipelines: Develop robust ETL and ELT pipelines for banking, treasury, payments, export finance, and other data sources.
 * Architecture: Design Data Lake and Lakehouse structures as well as scalable and high performance data models.
 * Data Quality: Implement automated quality checks and ensure data completeness, timeliness, and consistency.
 * Monitoring: Build monitoring and alerting for pipelines, jobs, latency, and errors.
 * Governance: Document data flows and lineage and support data classification, access controls, and regulatory requirements.
 * AI Enablement: Provide suitable data and interfaces for AI use cases and MLOps.
 * Collaboration: Support business teams with data analysis and work closely with Architecture, Platform, Risk, and Compliance.