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# Sustainability Data Analyst

- **Company:** Techtronic Industries - TTI
- **Location:** Stuttgart, Germany
- **Contract:** Permanent contract
- **Skills:** Data Analysis, C++ (Programming Language), Data Governance, Data Profiling, Data Visualization, Python (Programming Language), Power BI, SAP (Applications), Qliksense, Microsoft Power Automate, Vba Programming Language
- **Published:** August 19, 2026
- **Apply:** https://www.stepstone.de/stellenangebote--Sustainability-Data-Analyst-f-m-d-Winnenden-Techtronic-Industries-ELC-GmbH--14407112-inline.html

## About the Role

* Bachelor's or similar degree in Data Science, Environmental Science, Sustainability Management, or a related field, or equivalent professional experience \n
 * Proven experience with Power BI, Power Automate, VBA, C++ programming, Python, and SAP QuickViewer \n
 * Good communication skills in German and English, both written and spoken \n
 * Knowledge of Qlik Sense and IBM Envizi is considered an advantage \n
 * Relevant experience in data analysis, reporting, and data visualization \n

## Description

* Collaborate with business stakeholders and leadership teams to gather requirements, develop, and support data modelling and BI solutions for organizational stakeholders \n
 * Integrate different data sources into a cohesive data fabric, ensuring data consistency, accuracy and reliability \n
 * Conduct business analysis activities to understand business user requirements and translate data and information flows within business processes \n
 * Analyze sustainability, quality and regulatory data to identify trends, patterns and areas for improvement \n
 * Provide insights and recommendations based on data analysis to support informed decision-making in sustainability initiatives \n
 * Perform data profiling, validation and cleaning to ensure accuracy, completeness, and quality of enterprise data \n
 * Maintain data quality standards and enforce data governance policies, standards and best practices \n
 * Maintain clear documentation for data orchestrations, highlighting their value and contribution to improved data modeling and governance \n


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