Data Product Manager, CMC Digital

Here's What You'll Do

• Own the prioritization and sequencing of data products and AI use cases, grounded in measurable business value
• Define data products end-to-end, from use case to production, including consumers, SLAs, and acceptance criteria
• Ensure each data product has defined ownership, classification, access, and lineage, aligned with the organization's data governance framework
• Ensure data products are production-ready and operationally viable, not just analytically correct
• Define data quality, validation, and traceability requirements to ensure products are trusted and compliant in a GxP environment
• Drive adoption within operational workflows by measuring usage, gathering feedback, and iterating to ensure sustained value
• Apply an AI-first mindset, identifying opportunities to augment workflows and defining the data required to enable them
• Maintain a living product backlog with each item traceable to a business outcome and a data readiness state
• Partner with engineering teams to ensure appropriate testing, monitoring, and lifecycle management of delivered products
• Coordinate across data engineering, platform, analytics, and business teams to manage dependencies and communicate tradeoffs clearly

Here's What You'll Need (Minimum Qualifications)

• Bachelor's or Master's degree in Engineering, Computer Science, Life Sciences, Data Science, or a related field
• 4+ years of experience in pharmaceutical, biotech, or other regulated industries, with exposure to CMC domains
• 6+ years of experience defining and delivering data, analytics, reporting, or digital solutions used in production workflows
• Familiarity with modern data concepts including data modeling, data quality, and cloud-based data platforms
• Strong understanding of pharmaceutical systems and relevant data domains (e.g., Veeva, SAP, MES, LIMS)
• Proven ability to translate business needs into clear, structured requirements that engineering teams can build from without extended clarification cycles
• Demonstrated ability to produce written specifications, use case definitions, or data product briefs that serve as the primary handoff to builders
• Experience working across business and technical stakeholders, with the ability to drive alignment and communicate tradeoffs
• Experience with data governance principles including data ownership, classification, access control, and quality standards as applied to enterprise data products
• Understanding of GxP, data integrity, and compliance considerations for data, analytics, and AI solutions

Here's What You'll Bring to the Table (Preferred Qualifications)

• Direct experience with Veeva Quality, LabVantage, Syncade, or similar MES and LIMS platforms and their data structures
• Exposure to dbt, Redshift, Snowflake, or similar transformation and warehouse tooling
• Experience with medallion data architecture patterns
• Experience with GitHub-based workflows, docs-as-code practices, or version-controlled specification management
• Track record of driving adoption of data products beyond initial delivery into sustained operational use
• Experience participating in or contributing to a data governance program, including defining data standards, stewarding data products, or working with governance bodies across business and digital
• Curiosity about or experience with agentic AI workflows, including how data products, specifications, and operational knowledge can be structured to be consumed and acted on by AI agents, not just human analysts
• Familiarity with AI/ML use case evaluation, including RAG pipelines, LLM application design, or predictive model prerequisites
• Background in GxP validation readiness for data systems and analytics platforms