Senior Manager, Supply Chain Data Analytics & Governance

CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com.

What You’ll Do:

The Supply Chain Strategy & Transformation (SCST) team is responsible for designing and scaling the operating model for how CoreWeave plans, sources, builds, and delivers data center capacity for our customers. We partner closely with Supply Chain, Product Engineering, Data Center Operations, and the Enterprise Engineering teams to turn strategy into execution and measurable impact. 

We are building a dedicated Supply Chain Data, Analytics, and Governance function within SCST to own the data, analytics, and AI portfolio for Plan, Source, Build, Deliver, Sustain, and to define how Master Data is created, governed, and used across the network.

About the Role: 

As the Senior Manager, Supply Chain Data, Analytics, and Governance, you will own the data vision and operating model for Supply Chain and Data Center Operations. You’ll define how critical master data (e.g., parts, locations, suppliers) is created and governed, partner closely with the central Data & AI organization on platforms and pipelines, and prioritize analytics, data science, and AI agent work that makes it easy for teams to run and improve the business. You’ll shape a hub-and-spoke governance model that balances strong guardrails with pragmatic self-service for Plan, Source, Make/Build, and Deliver teams. This role reports into the Senior Director, Supply Chain Strategy & Transformation and is based out of Sunnyvale, CA or another CoreWeave hub (Sunnyvale strongly preferred, with flexibility for other hubs).

In This Role You Will:

• Set and maintain the supply chain data strategy and roadmap across MDM, analytics, data science, and AI agents.
• Design and run the supply-chain data and master-data governance model, including standards, stewardship, and issue management.
• Collaborate with the Data & AI team in IT on ingestion, modeling, and tooling so that supply chain data is clean, well-modeled, and easy to use.
• Identify and deliver high-value analytics and AI/agent use cases that improve visibility, decision speed, and predictability across the supply chain.

Who You Are:

• Significant experience owning master data and data governance for at least one major domain (e.g., parts/SKUs, suppliers, locations, BoMs), including standards, stewardship processes, and data-quality metrics.
• 8–10+ years in data, analytics, or data science roles, including 4–5+ years working with supply chain, operations, manufacturing, or other complex networked systems.
• 3+ years managing multi-disciplinary data teams (e.g., data engineers, analysts, data scientists, or similar) in a product, platform, or analytics context.
• Proven track record of defining and executing a data/analytics strategy for a business function including roadmap definition, prioritization, and delivery of measurable business outcomes.
• Experience partnering with a central IT / Data & AI organization on ingestion, transformation, and modeling, and helping avoid redundant transformation layers and fragmented schemas.
• Familiarity with delivering machine learning and/or AI-powered solutions (e.g., forecasting, optimization, anomaly detection, LLM/agent-based workflows) into production environments with appropriate controls and monitoring.
  
  

Preferred:

• Strong SQL skills (ideally with experience on Postgres or similar) and fluency in at least one analytics programming language (e.g., Python); comfortable reviewing technical designs and occasionally rolling up your sleeves.
• Experience with modern cloud data stacks, including:
• Columnar analytical databases or data lakes (e.g., StarRocks, Snowflake, BigQuery, Redshift, or similar).
• ELT tools and patterns (e.g., Fivetran or similar) and transformation frameworks such as dbt.
• Experience in cloud infrastructure, data centers, hardware/semiconductor, or other complex, capital-intensive supply chains.
• Hands-on experience with PLM/PDM and/or MDM platforms and their data models.
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