Data Engineer - Process Analytics & Data Intelligence

Data Engineer - Process Analytics & Data Intelligence

Lonza

Visp, Switzerland

Within the Process Analytics & Data Intelligence (PADI) team, embedded directly in Operations, we build production-ready data infrastructure that connects process, execution, and development data into a coherent and trusted foundation used daily by MSAT, Process Development, Operations, and global data teams.

We are looking for a Data Engineer – Process Analytics & Data Intelligence to design, build, and operate this data foundation and support reliable, scalable use of operational data across teams.

What You Will Do:

• Design, build, and operate automated data pipelines integrating distributed file-based data stores and raw operational system databases, including historians (PI), MES, ELN and instrument data, into a coherent, end-to-end operational data foundation;
• Replace manual, file-based data handling with governed, versioned and traceable data flows that stand up to daily operational use;
• Implement and enforce data quality checks, validation rules and monitoring to ensure data reliability at scale in an operational environment;
• Deliver analytics-ready datasets (clean schemas, consistent semantics, time-aligned data) to downstream BI, advanced analytics and modeling layers supporting operational decision-making;
• Work closely with MSAT, Process Development, Automation, QA, IT and global data teams to align data models, definitions and integration patterns across the organization;
• Continuously improve pipeline performance, robustness and maintainability in line with operational priorities.

Who You Are:

• You think in pipelines, schemas and failure modes, not files and manual workarounds;
• You design repeatability, observability and scale in environments where reliability matters;
• You are comfortable working across OT systems, MES, and cloud data platforms;
• You take end-to-end ownership, from source systems to analytics consumers;
• You simplify complex operational data landscapes without losing critical process detail.

What You Bring:

• Hands-on experience building and operating production data pipelines in operational or industrial environments;
• Strong SQL expertise and experience optimizing queries for operational workloads;
• Proven experience with ETL/ELT pipelines handling process and execution data;
• Hands-on exposure to historians (PI), MES, instrument data, or similar OT systems;
• Solid understanding of data quality, lineage, traceability, and governance ideally in regulated (GxP) environments;
• Experience with cloud data platforms, preferably Azure, including building and managing data pipelines (Azure Data Factory), data storage solutions (Azure Data Lake/Blob Storage), and version control using Git;
• Background in biopharma, manufacturing, or operations data is strongly preferred;
• Education: Degree in Engineering, Data Science, Computer Science, or a related technical field preferred — equivalent operational experience will be considered.