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# Data Engineer

- **Company:** Siemens Energy
- **Location:** Berlin, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Business Analytics Applications, Data Integration, Data Integrity, R (Programming Language), Python (Programming Language), Power BI, SAP (Applications), SAP NetWeaver Data Management, SAS (Software), Statistical Process Control (SPC), Tableau (Software), Technical Data Management Systems, Yield Optimization, Information Technology, Alteryx
- **Published:** October 3, 2026
- **Apply:** https://www.arbeitsagentur.de/jobsuche/jobdetail/15170-450532401-S

## About the Role

* Degree in Data Science, Statistics, Engineering, or a related technical discipline
 * Several years of hands-on experience in advanced analytics within a manufacturing environment
 * Proven experience in statistical analysis methods and tools (Python, R, SAS) and BI platform expertise (Tableau, Power BI, Alteryx)
 * Familiarity with SAP data structures and manufacturing IT systems
 * Understanding of blades and vanes manufacturing processes, including quality control and inspection workflows
 * Ability to work effectively in Agile, cross-functional teams and clear communication with technical and business stakeholders

## Description

As a Manufacturing Data Scientist (f/m/d) - Blades & Vanes, you will transform complex manufacturing, inspection and IoT data into actionable insights that drive quality, efficiency, and innovation in our turbine components production. You will work closely with engineering, operations and quality teams, building advanced analytics models and automation solutions that enable data-driven decision-making across the blades and vanes value chain. Your work will support process optimization, defect reduction and yield improvement - making a direct impact on our manufacturing excellence.

How You'll Make an Impact

 * Collection, consolidation, and integration of data from SAP, MES, PLM, inspection systems, and IoT sensors
 * Ensuring data integrity, consistency, and compliance with Siemens Energy governance standards
 * Development and maintenance of structured datasets and analytical models for production use cases, including process capability studies and statistical quality control
 * Application of advanced data science methods for trend identification, anomaly detection, and root cause analysis to support continuous quality improvement
 * Design and deployment of analytics solutions using Python, R, and BI tools (Power BI, Tableau), including automated reporting and real-time dashboards
 * Collaboration with engineering and operations teams for the implementation of data-driven solutions and the translation of insights into process improvement and cost optimization


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