Senior Principal Digital Factory Solutions Architect

Are you ready to orchestrate the digital threads of pharma manufacturing?

Imagine launching a new product analytics stream: you map historian tags to batch context, align ERP master data, integrate LIMS results, and configure Electronic Batch Systems so that Advanced Analytics models receive clean, timely inputs. By week’s end, you’ve published latency dashboards spanning LAN, WAN, and edge nodes in regional data centers, and presented a concise update to stakeholders across Global MSAT and IT Governance.

Your mission

• Design and manage data and software configurations that underpin complex pharmaceutical batch analytics.
• Ensure platform choices exhibit the right fit, form, and function through strong business insight and working knowledge of data science.
• Serve as the departmental owner for multiple initiatives—deliver independently, on schedule—and produce industry-standard reports that meet internal compliance needs and support regulatory submission.
• Provide architectural configuration guidance across process historians, ERP, LIMS, and Electronic Batch Systems for projects with diverse complexity.
• Evaluate and deploy commercial and open-source technologies that elevate the data science experience across sites and programs.
• Perform periodic infrastructure reviews of LAN/WAN paths and edge servers; analyze transfer rates; summarize data flow latency to optimize user experiences for scientists and local users.
• Advance agile practices in software and data management to support both standard and advanced data science programs and product groups.
• Act on feedback loops with Global MSAT, strategic site stakeholders and leadership, IT Governance bodies, and Global Engineering to refine program delivery.
• Explain data management requirements and capabilities in clear terms, tying them to Advanced Analytics outcomes for stakeholders at all levels and locations.
• Stay current by interpreting primary literature, engaging trade forums and user communities, and adapting emerging technologies to strengthen Data Analytics Product Groups.

What success looks like

Reduced latency across data flows, consistent configuration patterns for core systems, and delighted analytics teams who can trust the data foundation you’ve built.