Advisor - Data Strategy and Orchestration - Lilly Medicine Foundry

Role Summary

The Advisor - Data Strategy and Orchestration leads the design and implementation of innovative data architectures and governance for the Medicine Foundry, advancing pharmaceutical data management and enabling automated, paperless, integrated operations. This principal-level technical expert collaborates with Tech@Lilly architects and external partners to deliver data fabric, real-time streaming, ML-ready infrastructures, and regulatory-compliant data governance. The role supports GMP operations and site culture through dynamic, cross-phase project delivery and startup activities.

• Research, design, and implement novel data architecture approaches for pharmaceutical manufacturing that advance industry capabilities, including data mesh architectures, data fabric platforms, real-time streaming solutions, and cloud-native data services.
• Develop innovative technical solutions for ML-ready data infrastructures including feature stores, MLOps platforms, model registries, experiment tracking systems, and automated ML pipeline orchestration that enable advanced analytics and AI applications.
• Design and implement advanced data platforms employing modern technologies including lake houses (Databricks, Snowflake), distributed processing (Spark, Dask), streaming architectures (Kafka, Pulsar), and cloud-native services for scalable, high-performance data infrastructure.
• Collaborate with Tech@Lilly principal architects and engineers as a technical peer expert to design enterprise data architecture, evaluate emerging data technologies, and make platform decisions that balance innovation with operational requirements.
• Partner with the Augmented Intelligence Leader to build technical infrastructure enabling AI/ML innovation through proper data preparation, feature engineering capabilities, model governance frameworks, and responsible AI implementation.
• Develop innovative data governance frameworks and technical solutions that achieve cGMP compliance (21 CFR Part 11, EU Annex 11, ALCOA+) while enabling agility, including automated data quality monitoring, intelligent validation, and real-time audit trails.
• Drive research collaborations with academic institutions, industry consortia, and technology partners to explore emerging data management methodologies and establish thought leadership that positions Medicine Foundry as a leader in pharmaceutical data innovation.
• Design novel approaches to data democratization and self-service analytics in regulated environments, including semantic layers, knowledge graphs, automated metadata management, and AI-powered data discovery that enable business users while maintaining compliance.
• Establish enterprise data standards, metadata frameworks, and master data management approaches that ensure seamless data flows across MES, LIMS, SAP, Sample Management, and other digital plant systems supporting the unified digital thread vision.
• Lead multi-functional data governance council with PR&D, M&Q, QC, QA, and Engineering representatives to align on data requirements, quality metrics, integration approaches, and technical solutions for data integrity and regulatory compliance.
• Build and mentor a high-performing team of data professionals, coaching them in advanced data technologies, innovative problem-solving approaches, and technical excellence while fostering their growth in specialized areas.

• Required: Bachelor’s, Masters or PhD in Data Science, Computer Science, Data Engineering, or related technical experience.
• Required: Years of experience: BS - 12+ years, MS - 8+ years, PhD - 2+ years in data architecture, data engineering, or related fields with proven track record of researching, developing, and implementing novel data solutions in complex environments.
• Required: Deep hands-on technical expertise in cloud platforms (AWS, Azure, GCP), distributed systems (Spark, Dask), streaming architectures (Kafka), and advanced data integration patterns with proven ability to design and implement enterprise-scale solutions.
• Required: Research-level expertise in data architecture methodologies including data mesh, data fabric, lakehouse architectures, real-time analytics, and ML infrastructure with demonstrated history of advancing beyond industry standard practices.
• Required: Strong knowledge of pharmaceutical M&Q systems (MES, LIMS, ERP, Sample Management) and experience designing data architectures that integrate complex manufacturing and quality systems while maintaining regulatory compliance.
• Preferred: Experience in pharmaceutical, biotechnology, or FDA-regulated industries with deep understanding of cGMP data integrity requirements and regulated manufacturing data environments.
• Preferred: Track record of technical innovation demonstrated through publications, patents, conference presentations, open-source contributions, or recognized achievements in advancing data management methodologies in pharmaceutical or life sciences domains.
• Preferred: Deep hands-on experience…