Sr. Engineering Manager, ASE Efficiency - Jobs - Careers at Apple
- Define the strategy, technical direction, roadmap, and operating model for Demand Management across ASE Efficiency.
- Lead and grow an organization of approximately 12 individual contributors and one Engineering Manager. Hire, coach, and develop engineers, managers, and senior technical leaders.
- Remain technically hands-on through architecture reviews, demand modeling, telemetry analysis, and capacity validation, working through difficult technical problems alongside the team.
- Establish repeatable methods for translating product launches, customer and usage growth, workload characteristics, and resilience requirements into measurable infrastructure demand.
- Build scalable software, tooling, and automation for demand capture, validation, BOM definition, inventory visibility, prioritization, and exception handling. Drive adoption across service teams.
- Ensure demand entering planning and procurement is technically validated, attributable to clear drivers, and supported by explicit assumptions and business and financial context.
- Partner with Forecasting to separate feature-driven demand, organic growth, and operational uncertainty. Work with Foundation on data quality, telemetry, attribution, and tooling for capacity decisions.
- Identify capacity risks and drive improvements in application architecture, configuration, resource allocation, and utilization while preserving service reliability and performance.
- Measure demand accuracy, planning fidelity, utilization, adoption, and efficiency outcomes. Distinguish modeled opportunities from verified capacity reductions and realized financial savings, and feed results back into planning.
- Partner with Finance, Infrastructure, SRE, procurement, and business teams to align validated demand with funding, supply constraints, and delivery timelines. Communicate portfolio-level investment priorities, tradeoffs, and risks to senior leadership.
- 10+ years of software engineering, infrastructure engineering, SRE, capacity engineering, or related technical experience.
- 5+ years of engineering management experience, including managing managers and/or senior technical leaders, with a track record of hiring, developing, and retaining engineering talent.
- Experience leading engineering teams responsible for large-scale distributed systems, cloud infrastructure, platform engineering, or infrastructure management.
- Strong software engineering background and experience building or operating production systems, platforms, tooling, or automation used by multiple engineering organizations.
- Ability to reason deeply about application architectures and distributed systems across compute, memory, storage, networking, databases, and containerized environments, and connect workload behavior to infrastructure requirements.
- Strong analytical judgment and experience using telemetry, utilization, and performance data to validate requirements and guide engineering decisions.
- Demonstrated ability to set technical strategy, establish engineering standards, and drive adoption across organizational boundaries.
- Excellent written and verbal communication, with experience influencing senior engineering, infrastructure, finance, and business stakeholders and explaining technical and financial tradeoffs.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent practical experience.
- Experience leading capacity management, infrastructure efficiency, performance engineering, demand forecasting, FinOps, or resource management teams.
- Experience connecting product roadmaps, feature launches, customer growth, and business forecasts to infrastructure planning for large-scale consumer or cloud services.
- Experience developing capacity modeling, forecasting, automated sizing, optimization, or infrastructure intelligence platforms.
- Experience with Kubernetes, distributed data systems, storage platforms, traffic management, or observability systems, including data quality and attribution across shared infrastructure.
- Understanding of infrastructure economics and supply chains, including capital investment, cloud spend, cost attribution, procurement, hardware deployment, and data center constraints.
- Experience applying machine learning, GenAI, or LLM-assisted tooling to infrastructure analysis, automation, forecasting, or engineering productivity.
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Required Languages
🇬🇧 English