Staff Technical Program Manager Data Center Data and Tooling
Cerebras Systems, Inc.Visit Cerebras Systems, Inc. website
Cerebras builds wafer-scale AI computing systems and a cloud inference platform for training, fine-tuning, and serving AI models.
Series F+Recently funded
Sunnyvale, California, United States
About Cerebras Systems, Inc.
Cerebras Systems is an AI-infrastructure company founded in 2015. It sells rack-scale wafer-scale computing systems and provides cloud-based, API-accessible AI inference alongside on-premises deployments.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will lead the roadmap for data and tooling that support planning, delivery, and operational handoff. You will create shared data models, integrate operational information, build analyses and dashboards, implement tools, improve data quality, and communicate program decisions and risks.
Requirements
- Experience leading complex technical programs across infrastructure, enterprise systems, data platforms, or large-scale operations
- Ability to set direction across multiple teams and resolve competing priorities
- Strong SQL skills
- Experience validating datasets, defining metrics, and developing operational dashboards
- Working knowledge of Python or comparable scripting
- Ability to discuss APIs, data models, integrations, workflow design, and data quality requirements
- Experience connecting operational data to schedules, capacity, cost, or equipment readiness
- Strong written and verbal communication
Responsibilities
- Own the data and tooling roadmap for the Data Center organization
- Define shared data models, metric definitions, data ownership, and update expectations
- Integrate project, procurement, inventory, financial, and deployment information
- Build and validate SQL analyses and automation
- Drive dashboards for progress, forecasts, and delivery constraints
- Lead tool evaluation, implementation, testing, rollout, documentation, and training
- Establish data quality checks, reconciliation processes, change history, and access requirements
- Drive adoption, remove redundant reporting, and measure operational improvements
- Present program status and tradeoffs and escalate unresolved dependencies