Data Engineer (m/f/d)

Job description

Data Engineer Are you technically skilled, enjoy exploring new approaches, and want to avoid being held back by existing structures and processes? As a Data Engineer , you will join an environment where many things are being built from the ground up and where you can actively contribute your own technical ideas. From architecture and data pipelines to data quality and AI interfaces, you will have the opportunity to develop modern solutions while taking ownership of their implementation. Apply now and take advantage of the technical freedom this position offers. Your benefits as Data Engineer Compensation: Enjoy an attractive salary with an additional variable component that reflects your experience and technical expertise. Creative Freedom: Help build a central data platform from the ground up and bring your own technical ideas to the table. You will have plenty of room to explore modern solutions and actively shape the technical architecture. Flexibility: Organize your working day largely on your own and work flexibly from home or anywhere in Germany. At the same time, you should be willing to attend important meetings, workshops, and in person collaboration when required. Workation: Take up to four weeks of workation within the EU and, subject to agreement, potentially beyond the EU. Pension: Benefit from an occupational pension scheme with a 50% employer contribution. Development: Take advantage of individual training opportunities, language courses, and personal learning programs. International Environment: Work in an international environment where both German and English are used as business languages. Your tasks as Data Engineer 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…