Lonza

Overview As a Data Engineer in the Process Analytics Data Intelligence (PADI) team in Visp, you design, build, and operate the operational data foundation that powers MSAT, Process Development, Operations, and global data teams. You replace manual data handling with governed, traceable data flows and deliver analytics-ready datasets for downstream consumption. You work with cross-functional teams to align data models and improve pipeline performance. This role offers exposure to OT, MES, PI data, and cloud data platforms to enable reliable decision-making in manufacturing. You will make a measurable impact on data reliability and operational insights.

Leistungen / Benefits Relocation assistance for eligible candidates and families Verantwortungsbereiche Design, build, and operate automated data pipelines across distributed data stores and OT systems (PI, MES, ELN, instrument data) Replace manual data handling with governed, versioned data flows Implement data quality checks, validation, and monitoring for scalable reliability Deliver analytics-ready datasets with clean schemas and time-aligned semantics to BI and analytics layers Collaborate with MSAT, Process Development, Automation, QA, IT, and global data teams to align data models and integration patterns Continuously improve pipeline performance, robustness, and maintainability Zentrale Anforderungen Hands-on experience building and operating production data pipelines in operational/industrial environments Strong SQL expertise and optimization for operational workloads Proven

ETL/ELT experience handling process and execution data Experience with historians (PI), MES, instrument data, or similar OT systems Understanding of data quality, lineage, traceability, and governance (ideally in regulated (GxP) environments) Experience with cloud data platforms, preferably Azure (Azure Data Factory, Data Lake/Blob Storage) and Git for version control Background in biopharma, manufacturing, or operations data preferred Degree in Engineering, Data Science, Computer Science, or related field preferred or equivalent operational experience End-to-end ownership Systems thinking and problem solving Cross-functional collaboration SQL ETL/ELT pipelines Azure Data Factory