Program Manager II, Logistics Planning, Technical Infrastructure
Confirmed on the employer's own hiring board on Oct 15, 2026. First seen by Alion on Oct 15, 2026. Google scores B on the Alion truth index.
About the job
A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Program Manager at Google, you’ll lead complex, multi-disciplinary projects from start to finish - working with stakeholders to plan requirements, manage project schedules, identify risks, and communicate clearly with cross-functional partners across the company. Your projects will often span offices, time zones, and hemispheres. It's your job to coordinate the players and keep them up to date on progress and deadlines.
As a Program Manager, you will own the day-to-day modeling, analysis, and cross-functional coordination for logistics network readiness across our global fulfillment nodes and data center footprint. You will bridge the gap between tactical execution and medium-to-long-term planning by maintaining and executing quantitative models for throughput rates, spatial/storage constraints, dynamic labor requirements, and network flow balance.
You will use SQL, data pipelines, and spreadsheet/analytical models to evaluate operational run-rates, stress-test finite node capacities (dock turnover, receiving/staging space, rack/pallet footprint, processing hours, labor shifts), and flag operational risks before they impact execution. In addition, you will serve as a key business SME for internal engineering and tools teams, translating day-to-day network parameters and constraint logic into automated short- and long-term planning workflows.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $132000 - $189000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Own and maintain deterministic node-level capacity and constraint models across warehouses and data centers (dock processing rates, receiving/staging capacity, storage/rack/pallet footprint, pick/pack/kitting speeds, and hand-off velocity).
- Monitor operational health through dashboards, highlighting network readiness gaps, shift shortages, or processing risks well in advance of execution.
- Perform quantitative scenario analysis and sensitivity checks across both tactical demand supply review cycles and medium-to-long-term planning horizons to model the operational impact of volume surges, intake delays, or schedule adjustments.
- Conduct root-cause analyses on execution shortfalls, identifying whether variances stemmed from forecast deviation, labor availability, or processing inefficiencies.
- Serve as a key operational partner to internal cross-functional stakeholders/operations, providing validated business logic, table schemas, and functional rules to automate manual planning sheets across short- and long-term planning modules.
Qualifications
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 2 years of experience in program or project management.
- Experience in supply chain logistics and capacity planning, network modeling, or constraint planning.
- Experience with SQL and quantitative modeling.
Preferred qualifications:
- 2 years of experience managing cross-functional or cross-team projects.
- Experience providing operational requirements, constraint logic, or user-acceptance testing (UAT) to engineering teams building automated short- and long-term supply chain planning tools.
- Proficiency in SQL (writing complex queries, joining multi-table datasets) and familiarity with scripting languages (e.g., Python, R) for data wrangling, automation, or simulation/optimization routines.
- Demonstrated ability to conduct root-cause analysis on operational performance data (plan-to-actual variance tracking, productivity metrics, cycle time analysis).
- Ability to conduct root-cause analysis on operational performance data (plan-to-actual variance tracking, productivity metrics, cycle time analysis).