Digitalization Engineer
Pubblicato il 17-08-2026 - Gea Group in Parma
Are you ready to shape the future of sustainable industries?
Join GEA and become part of a global team that’s driving innovation, excellence, and impact across the food, beverage, and process sectors.
GEA is one of the leading suppliers for the food and beverage processing industry and for a wide range of other process industries. Around 18,000 employees in over 60 countries contribute significantly to GEA’s success: join them We offer interesting and challenging tasks, a positive working environment in international teams, and opportunities for personal development and growth in a global company.
Responsibilities / Tasks
As a member of the
Technical Software Tools
team, the candidate will be placed within GEA’s
Engineering Department
and will be required to:
- Actively contribute to process redesign.
- Provide cross-functional support to introduce innovation and digital solutions.
- Support and enable data management and process improvement activities, including user training.
- Monitor the performance of digital solutions currently in use and propose continuous improvements.
- Design and develop digital applications and dashboards aligned with business needs.
- Ensure scalability of solutions according to business and functional needs.
- Proactively identify opportunities for process optimization and digitalization.
- The role will play a key part in the ongoing ERP transformation, contributing to the analysis, restructuring, and optimization of business data and processes.
- Participate in weekly team activities.
- Ensure alignment with quality, safety, and cybersecurity standards.
Technical Skills
- Strong understanding of relational database design principles (data modeling, normalization, performance optimization).
- Hands-on experience with SQL Server (querying, data extraction, data manipulation).
- Solid knowledge of data analysis techniques, with the ability to work on complex datasets and support data-driven decision-making.
- Experience in data analy