Джерело: CrawlJobs
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Capacity Planning Engineer

Hybrid work

Publication date: 12/11/2026

The Demand Planning team sits at the intersection of traffic forecasting, infrastructure strategy, and operational execution. We predict when, where, and how many servers to deploy across Cloudflare's global edge so that customers' traffic stays in-region. This is a planning and engineering role that requires you to build the models, own the data, and translate forecasts into real deployment decisions. As a Capacity Engineer, you will own one or more planning regions end-to-end with light supervision. This role is for someone with 3-5 years of experience who is ready to operate with real ownership and is actively growing toward full autonomy.

Responsibilities

Own one or more edge planning regions end-to-end, producing deployment recommendations with minimal supervision; Maintain and continuously improve regional planning models, incorporating new traffic patterns, hardware generations, and infrastructure changes; Build and maintain data pipelines that feed regional planning using BigQuery, Python, and Jupyter notebooks; Work directly with hardware sourcing, DCS, and infrastructure teams to translate forecasts into actionable deployment plans; Proactively identify capacity risks in your region and surface them before they require escalation; Use OpenCode AI and MAS agent tooling as a standard part of the daily planning workflow; Contribute to cross-regional consistency efforts alongside peers; Participate in planning reviews and communicate forecast outputs clearly to non-technical stakeholders

Requirements

3-5 years in an analytical, planning, or technical infrastructure role; Working SQL proficiency, comfortable querying and joining datasets in BigQuery; Python proficiency for data analysis, scripting, and automation of planning workflows; Comfort working in Jupyter notebooks; Comfort working with large infrastructure datasets and translating them into decisions; Clear written and verbal communication; Demonstrated ability to own a workstream end-to-end with light supervision
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