Entry-Level Data Pipeline Engineer – Manufacturing
Bayer AG
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Job summary
Bayer AG is seeking a technically skilled professional to lead data pipeline development and integration within a manufacturing quality systems context. You will collaborate with engineers, IT and end users to modernize our data stack, build robust datasets, and deliver actionable insights for the shop floor.
The role emphasizes cross-functional collaboration, strong technical acumen in Pipeline Pilot, SpotFire, and BI tools, plus experience with data virtualization and DevOps practices to
Qualifications
- 3–5 years experience in Manufacturing, Life Science, Quality Systems or related field
- 3–5 years building production solutions with Biovia Pipeline Pilot
- 3+ years with Spotfire or PowerBI
- Experience in QA environments
- History of managing complex projects and meeting deadlines
- Self-led technology discovery experience
- Experience with data virtualization preferred (Denodo)
Responsibilities
- Design and deliver maintainable data pipelines
- Develop and maintain solutions using Pipeline Pilot and SpotFire
- Build datasets for visualization and reporting with accuracy and traceability
- Document data lineage for visualizations
- Support DevOps and deployment infrastructure
- Assist in sprint planning, ticket management, and bug tracking
- Create data visualizations and reporting based on stakeholder feedback
- Collaborate with engineers, IT, and end users across sites
- Optimize data workflows for manufacturing operations
- Communicate complex projects to cross-functional teams
Skills
Education
Tools
Job description
Bayer AG is seeking a technically skilled professional to lead data pipeline development and integration within a manufacturing quality systems context. You will collaborate with engineers, IT and end users to modernize our data stack, build robust datasets, and deliver actionable insights for the shop floor.
The role emphasizes cross-functional collaboration, strong technical acumen in Pipeline Pilot, SpotFire, and BI tools, plus experience with data virtualization and DevOps practices to
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