Senior Manager, Manufacturing Systems Data Data Historian, Batch Reporting & Analytics
What you will do
This strategic global leadership role will be accountable for architecting, building, and evolving the Manufacturing Systems Data COE, overseeing deployment, lifecycle management, and governance of manufacturing data platforms, batch reporting solutions, and analytics across Amgens global manufacturing network.
As the Senior Manager, you will direct a high-performing team of data engineers, analysts, and solution architects, ensuring effective delivery and continuous advancement of Data Historian infrastructures, batch reporting solutions, and analytics platforms. You will partner closely with Digital, Technology & Innovation, Operations, Quality, IT, Automation, and Data Science leaders to drive Amgens next-generation manufacturing data strategyfrom vision through executionenabling seamless integration between PI, cloud, datalakes, reporting, and analytics platforms.
Your leadership will ensure data standardization, compliance, and analytics excellence, accelerating Amgens digital transformation, operational insight, and data-driven decision making on a global scale.
Roles & Responsibilities
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Lead and manage the global Manufacturing Systems Data team, including strategy, team building, performance management, and technical talent development.
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Design, implement, and execute the global strategy for PI Data Historian, batch reporting, and analytics platforms, ensuring tight alignment with Amgens business objectives and digital transformation initiatives.
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Establish and enforce best-in-class standards, methodologies, and governance for historian data architecture, data modeling, system integration, analytics, cybersecurity, data integrity, and regulatory compliance (e.g., GxP, CFR Part 11).
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Drive harmonization of batch reporting, data collection, and analytics business processes and technical implementations across global sites, leveraging ISA-95, ISA-88, and other relevant manufacturing standards.
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Guide the evaluation, adoption, and lifecycle of emerging technologies (e.g., Databricks, data lakes, data fabrics, AI/ML, containerization, real-time streaming analytics) to enhance analytics capabilities for advanced manufacturing use cases.
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Oversee solution design, deployment, and lifecycle management of PI Data Historian (OSIsoft/AVEVA), batch reporting platforms, and visualization tools (Spotfire, Tableau, Power BI) across all manufacturing sites.
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Champion the development of advanced analytics capabilities, including predictive maintenance, process optimization, and quality analytics using data science, software engineering, and AI/ML tools.
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Serve as a steering member and partner concern point for major data platform projects, system incidents, and strategic vendor relationships.
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Develop and manage COE resource plans, budgets, and external partnerships to ensure organizational objectives are met.
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Foster a culture of innovation, multi-functional knowledge sharing, and continuous improvement throughout the COE and the global manufacturing analytics community.
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Oversee comprehensive documentation, knowledge management, and training programs to support global adoption and sustainable value of data platforms and analytics solutions.
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Champion change management strategies to embed data-driven thinking and analytics adoption at all organizational levels.
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Represent Amgen Manufacturing Systems Data strategy and practices to senior leadership, regulatory agencies, and external technology partners as required.
What we expect of you
We are all different, yet we all use our unique contributions to serve patients.
Basic Qualifications:
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Doctorate degree / Master's degree / Bachelor's degree and 12 to 17 years Engineering, Computer Science, Data Science, Information Systems, or a related field
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Minimum 8 years of hands-on experience with PI Data Historian (OSIsoft/AVEVA), batch reporting, and analytics solution architecture, deployment, and lifecycle management in a GMP-regulated pharmaceutical, biotechnology, or manufacturing environment.
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Extensive experience with advanced analytics platforms and reporting (Spotfire, Tableau, Power BI) and integration with PI and manufacturing data sources.
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Proven track record leading global, multi-site manufacturing data transformation programs, including direct management and mentorship of technical teams.
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Deep understanding of GxP regulations, CSV/validation, and SDLC standard methodologies for manufacturing data systems.
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Demonstrated expertise with IT/OT cybersecurity, data integrity, cloud-based architectures, and modern data platforms (Databricks, data lakes, data fabrics, containerization, streaming analytics).
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Strong software development experience, with proficiency in Python, SQL, or other relevant programming/scripting languages for data engineering and analytics automation.
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Solid knowledge of AI/ML concepts and their application to manufacturing analytics (predictive analytics, anomaly detection, root cause analysis, etc.).
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Excellent financial and vendor management experience.
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Outstanding communication, relationship-building, and collaborator engagement skills at all levels, including executive leadership.
Preferred Qualifications
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Expertise in advanced OSIsoft PI/AVEVA historian modules (e.g., PI Asset Framework, PI Vision, PI Batch, PI Event Frames, PI Integrator for Business Analytics).
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Experience deploying data integration solutions between PI, SAP, MES, and automation platforms (Rockwell, DeltaV, OPC UA, Pi connectors/Interfaces, etc.).
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Proficiency with cloud-based analytics, Databricks, data lakes, and/or data fabric architectures for large-scale manufacturing data enablement.
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Professional certifications in Data Analytics, PI System, or Project Management (e.g., CAP, PMP, or equivalent).
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Experience representing manufacturing data programs with regulatory agencies and external partners.
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Familiarity with modern software development practices, DevOps, containerization (Kubernetes), and data science toolkits.
Soft Skills
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Visionary leadership with a passion for nurturing technical talent and strong analytics teams.
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Critical thinking, adaptability, and a growth mindset for leveraging data and analytics in manufacturing.
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Exceptional organizational, analytical, and complex problem-solving abilities.
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Resilience in a dynamic, fast-paced, and global environment.
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Commitment to diversity, inclusion, and fostering a collaborative data-centric culture.
