Director, Data Engineering & Analytics
About the role
Bristol Myers Squibb is seeking an experienced and highly motivated Director of Data Engineering to join the Digital Strategy & Process Optimization team within the Manufacturing Sciences & Technology (MS&T) organization. This role is crucial for driving strategic direction, delivery, and long-term sustainability of the PDS Data Spine and MS&T data ecosystem.
Responsibilities
Enterprise Product Owner for the PDS Data Spine and MS&T Data Suite with accountability for strategy, roadmap, funding prioritization, and value delivery.
Own CMO data connectivity standards and scalable integration patterns for external manufacturing data.
Own UDM integration for MS&T, ensuring alignment with enterprise data models and governance frameworks.
Act as key decision authority for MS&T data product scope, sequencing, and trade-offs.
Provide strategic and operational leadership to the MS&T Data Engineering team.
Oversee architecture and delivery of end-to-end data pipelines and analytics-ready assets forming the PDS Data Spine.
Ensure integration across internal manufacturing systems, CMOs, and enterprise UDM platforms.
Set standards for data quality, harmonization, observability, and lifecycle management.
Executive accountability for GxP-compliant MS&T data delivery.
Ensure data integrity, auditability, validation readiness, and security by design.
Partner with Quality, IT, Enterprise Data Governance, and External Manufacturing to manage risk.
Senior MS&T data representative to enterprise and PDS leadership forums.
Influence investment decisions and prioritize MS&T data outcomes.
Define and track outcome-based metrics demonstrating business and scientific value.
Enable AI/ML, digital twins, and self-healing manufacturing via a trusted data foundation.
Qualifications & Experience
Expected bachelor’s degree in a relevant discipline with a minimum 15 years of relevant work experience. In engineering or science (e.g., Process Engineering, Chemical Engineering, or Applied Mathematics/Statistics/Data Science. Multi-discipline is preferred).
Significant leadership experience delivering enterprise-scale data platforms in regulated environments.
Expertise with large-scale data processing platforms such as Databricks, Spark-based systems, and distributed compute frameworks used for batch and streaming data engineering.
Experience with enterprise data science & MLOps platforms such as Domino Data Lab (or equivalents) for reproducibility, model lifecycle management, auditability, and regulated-environment deployment.
Data governance, quality, and security leadership – Proven experience implementing data quality, observability, lineage, access control, and compliance frameworks across the data platform (especially in regulated industries).
Solid technical knowledge of unit operations associated with biologics and pharma manufacturing processes such as large-scale cell culture, protein purification, blending.
Experience in data systems such as OSI PI (PI Historian, PI Vision), Discoverant, LIMS, Datalake.
Working knowledge of Automation tools such as DeltaV, Syncade MES.
Experience with manufacturing process time series data, images, and spectra data.
Excellent interpersonal, collaborative, team building, and communication skills to ensure effective collaborations within matrix teams.
Demonstrated performance against cooperation principles and enterprise mindset.
Demonstrated problem solving ability, attention to details, and analytical thinking.
Exceptional communication skills Oral/Written.