Head of Planning Automation for Equipment Supply Chain

Google's projects, like our users, span the globe and require managers to keep the big picture in focus while being able to dive into the unique engineering challenges we face daily. As a Technical Program Manager at Google, you lead complex, multi-disciplinary engineering projects using your engineering expertise. You plan requirements with internal customers and usher projects through the entire project lifecycle. This includes managing project schedules, identifying risks and clearly communicating them to project stakeholders. You're equally at home explaining your team's analyses and recommendations to executives as you are discussing the technical trade-offs in product development with engineers.

Using your extensive technical and leadership expertise, you manage various Engineering-specific programs and teams.

In this role, you are a pragmatic visionary leader with exceptional ability to translate long-term infrastructure investment trade-offs into quantitative solvers. You possess the technical sophistication to build the digital frameworks that orchestrate data and insights in the highly complex multi-disciplinary investment of data center equipment planning. You have the technical sophistication to build data flows and process optimization in the AI Era. Within Data Center (DC) equipment planning, you will act as the critical technical translator bridging high-level business planning objectives with complex engineering execution. You will own the technical roadmap to transition our DC equipment planning from current planning solutions, manual, spreadsheet-based workflows into a highly integrated, automated AI-First software ecosystem. You will drive technical architecture across multiple cross-functional teams, perform system-level due diligence, and define foundational data models.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $240000 - $333000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google (https://www.google.com/about/careers/applications/benefits/).

Minimum qualifications:

- Bachelor's degree in a technical field, or equivalent practical experience.

- 10 years of experience in program management.

- 7 years of experience in leadership roles with/without direct reports.

Preferred qualifications:

- 10 years of experience in technical program management, software engineering, or data architecture driving large-scale system transitions.

- Experience in supply chain data architecture or large-scale enterprise planning engines (e.g., Kinaxis, SAP, E2Open).

- Experience in quantitative methods, applied statistics, data modeling, or operations research to effectively evaluate automated planning algorithms and predictive supply chain models.

- Proficiency in SQL/BigQuery, data schema design, data pipeline (ETL) management, and core troubleshooting.

- Track record of evaluating upstream and downstream software impacts across complex API integrations and distributed databases.

- Evaluate and govern how planning engine core upgrades and logic modifications impact downstream systems and transactional data pipelines. Architect and standardize foundational data schemas, APIs, and ETL pipelines across disparate environments to ensure latent-free, high-integrity data ingest.

- Review complex engineering and database designs to ensure they scale efficiently under global supply chain constraints without causing system latency or performance degradation.

- Harmonize business planning cycles with engineering sprint schedules and deployment freezes to prevent systemic disruptions.

- Architect programmatic, high-performance measurement systems like the Data Quality Index (DQI) using SQL/BigQuery to evaluate automated data-cleansing health (e.g., Site BOM availability, Sourcing Rule integrity).

- Build and scale automated validation frameworks to capture technical risks (such as data ingestion failures or logic anomalies) before code hits production environments.