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2 data engineering jobs found in Dortmund

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Deutsche Factoring Bank GmbH & Co. KG
29/04/2026
Full time
Data Engineer (m/w/d)
Deutsche Factoring Bank GmbH & Co. KG Ratingen, Germany
in Ratingen Factoring is playing an increasingly important role in the financing mix for modern companies. Deutsche Factoring Bank is part of the Deutsche Leasing Group and, as part of the Sparkassen-Finanzgruppe (Savings Banks Finance Group), is one of the leading institutions in the sector. Based on over 50 years of experience in national and international business, Deutsche Factoring Bank supports large and small companies, and especially medium-sized enterprises (SMEs) from over 50 industries, with modular service packages for financing, credit protection, and receivables management. Take advantage of a growing market! Apply now at Deutsche Factoring Bank, the center of excellence for factoring and receivables management. Your main tasks In our   IT Development group in Ratingen,   you will support the   Enterprise Data Warehouse and Business Intelligence (EDWBI) team   as a   Data Engineer (m/f/d)   in the MS SQL Server environment. Your main focus...
KOSTAL Automobil Elektrik GmbH & Co. KG
27/04/2026
Full time
Machine Learning Engineer / Edge-ML Automotive (m/w/d/kA)
KOSTAL Automobil Elektrik GmbH & Co. KG Dortmund, Germany
We are the KOSTAL Group, a globally operating, independent family business headquartered in Germany, developing and producing technologically sophisticated electronic and mechatronic products. More than 20,000 employees work flexibly, competently, and with a customer-centric approach at 64 locations in 22 countries. The KOSTAL Automotive Electrical division develops and produces power electronics for electromobility, control units for comfort electronics, and user interfaces ranging from turn signal switches to touchscreens. Job description Development of robust ML algorithms for intent/gesture recognition based on vehicle-side sensor data (e.g., UWB radar), including real-time inference on edge ECUs. End-to-end ownership of the ML pipeline: data acquisition on the prototype vehicle, ground truth/labeling, feature engineering, model training (PyTorch), experiment tracking (MLflow), model versioning and ensuring reproducibility Edge deployment:...
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