- Own the end-to-end capacity planning lifecycle for our platforms — demand forecasting, supply planning, utilization analysis, and efficiency optimization
- Drive monthly capacity forecasts — own the process end-to-end, ensuring timely and accurate submissions every cycle
- Submit and track incremental capacity requests — ensure new resource needs are captured, justified, and filed on a monthly cadence
- Represent the platform in all capacity-related forums and meetings — be the authoritative voice on current state, projected needs, and risks across cross-functional reviews
- Build and maintain capacity models that translate business growth signals into infrastructure requirements
- Use AI to build dashboards and reporting tools that surface capacity health, trends, and anomalies — making complex infrastructure data accessible to engineering and leadership
- Apply AI-driven analysis to predict resource needs and flag capacity risks before they become incidents — shifting the team from reactive firefighting to proactive planning
- Partner with service teams to understand workload characteristics, growth trajectories, and resource consumption patterns
- Identify and drive efficiency improvements — right-sizing, bin-packing, rebalancing, and decommissioning underutilized resources
- Establish and track capacity-related SLOs and KPIs (utilization targets, lead times, headroom thresholds)
- Represent capacity planning in procurement and budget cycles, providing data-driven recommendations for hardware acquisition
- Drive quarterly and annual capacity reviews, surfacing risks and opportunities to senior leadership
Minimum Qualifications
8+ years of experience in infrastructure engineering, capacity planning, SRE, or a related discipline
Experience with capacity planning at hyperscale or large enterprise infrastructure
Demonstrated experience forecasting infrastructure demand and translating it into actionable supply plans
Strong analytical skills — comfortable working with large datasets, building models, and drawing conclusions from utilization and performance data
Experience applying AI/ML techniques to operational or infrastructure problems — anomaly detection, demand forecasting, trend analysis, or similar
Ability to build AI-assisted dashboards and tooling that turn raw infrastructure data into actionable insights
Solid understanding of compute infrastructure concepts: virtualization, containers, orchestration (Kubernetes), bare metal, resource scheduling
Ability to communicate complex technical trade-offs to both engineering teams and non-technical stakeholders
Self-directed and comfortable operating with ambiguity — you define the process, not just follow it
Preferred Qualifications
Hands-on experience with LLMs, AI coding assistants, or generative AI for engineering productivity
Familiarity with infrastructure procurement and hardware lifecycle management
Exposure to time-series forecasting methods (statistical or ML-based)