Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Owns the master data strategy, governance, quality standards, source-of-truth definitions, and integration architecture for an AI infrastructure supply chain. The role manages material, supplier, customer, asset, site, project, and financial master data across ERP, PLM, procurement, quality, inventory, finance, and project-control systems. Responsibilities include defining stewardship and approval workflows, monitoring data-quality KPIs, resolving cross-functional conflicts, supporting data migrations and integrations, and reporting master data health to leadership.

Build and operate supply chain data pipelines, models, integrations, quality controls, and dashboards supporting procurement, inventory, logistics, suppliers, and project analytics. Partner with analysts and business teams to enable forecasting, planning, scorecards, KPI tracking, and data governance. Contribute to cloud data architecture, engineering standards, tooling implementations, and productionizing analytics models across AI infrastructure and capital-intensive supply chain operations.

Manages end-to-end supply chain contracting for HPC hardware, networking, fiber, construction, facilities, and energy suppliers. Coordinates sourcing, procurement, legal, and vendors; reviews and redlines agreements; administers Ironclad CLM workflows, templates, approvals, reporting, and repositories; maintains playbooks; tracks obligations, renewals, and expirations; assesses contract risk; supports negotiations; and improves contracting cycle time and data accuracy.

Designs and leads supply chain data architecture, ELT/ETL pipelines, integrations, dimensional models, data-quality controls, governance, and analytics enablement. The role supports ERP, procurement, inventory, logistics, supplier, and project data while establishing engineering standards and trusted datasets. Responsibilities include dashboards, forecasting foundations, supplier analytics, system implementations, security models, and productionizing data science solutions across capital-intensive AI infrastructure and data center operations.

Supports AI Cloud commercial operations and go-to-market execution, including customer engagement, opportunity evaluation, financial modeling, pricing, deal structuring, contract governance, pipeline management, forecasting, and executive reporting. Coordinates with engineering, operations, product, legal, finance, and project delivery teams to translate technical requirements into commercial outcomes and develop customer relationships and market intelligence.

Leads commercial operations and go-to-market execution for AI Cloud services. Responsibilities include evaluating customer opportunities, developing financial models and pricing analyses, supporting deal structuring and contract governance, managing pipeline and forecasting processes, building customer and partner relationships, and preparing executive and board-level materials. The role coordinates closely with engineering, operations, product, legal, finance, and project delivery teams to align technical requirements with commercial outcomes.

Leads commercial operations and go-to-market execution for AI Cloud services. Responsibilities include evaluating customer opportunities, developing financial models and pricing analyses, structuring deals, managing contracts and pipelines, coordinating cross-functional teams, building customer and partner relationships, and presenting AI strategy, performance, risks, and recommendations to executive and board-level audiences.

Owns PLM strategy and execution for AI infrastructure hardware, including platform selection, implementation, BOM governance, part and product master data, engineering changes, and configuration control. Leads PLM coordinators and partners with engineering, quality, sourcing, operations, IT, and finance to establish a controlled product-data source of truth. The role also governs standards, integrations, training, KPIs, supplier change notifications, and continuous improvement across the hardware lifecycle.

Owns product and manufacturing quality strategy for rack-scale AI and GPU/HPC systems from new product introduction through volume production. Defines test methods, quality standards, KPIs, and acceptance criteria; leads failure analysis, corrective actions, reliability, yield, and continuous improvement. Partners with engineering, suppliers, and OEM/ODM/JDM manufacturers to resolve system-level hardware issues, manage changes and BOMs, conduct inspections, and ensure cost, cycle-time, quality, and delivery targets. Frequent travel to manufacturing, supplier, integration, and data center sites is required.

Owns part master data and multi-level BOM structures for AI compute clusters throughout the product lifecycle. Maintains item records, revisions, effectivity, ECOs, ECNs, PCNs, service BOMs, and FRU/CRU structures in PLM systems. Partners with Engineering, Sourcing, Operations, OEMs, ODMs, and suppliers to validate data, support NPI, manage changes, resolve discrepancies, and ensure PLM-to-ERP alignment. Also audits data quality, improves standards and workflows, trains users, and mentors PLM coordinators.