Data Engineer - Capacity Planning - Apple Data Platform
Summary
Help us build the data foundation for capacity planning across Apple’s data and AI infrastructure. The Apple Data Platform organization is looking for a Data Engineer to build data pipelines, models, and analytical tools that help us understand infrastructure demand, utilization, capacity, and cost. The role will initially focus on third-party cloud infrastructure including GPUs, TPUs, compute, and storage and will expand over time to Apple-owned infrastructure. You will work closely with engineering, CIBO, Finance, and Procurement to help Apple make better infrastructure planning and investment decisions.
Description
As a Data Engineer focused on Capacity Planning, you will bring together infrastructure telemetry, workload demand, capacity commitments, and financial data into trusted datasets and data products. You will build the pipelines and analytical foundations used to understand current utilization, forecast future needs, identify capacity gaps, and improve infrastructure efficiency. You will partner with engineering and business teams to turn complex infrastructure data into actionable insights.
Responsibilities
- Build and maintain data pipelines for infrastructure capacity, utilization, performance, and cost data.
- Develop trusted data models for GPU, TPU, CPU, storage, and other infrastructure resources.
- Build cost models that calculate unit economics such as cost per GPU hour, cost per job, and cost per 1M tokens using measured production utilization.
- Integrate workload demand, utilization telemetry, capacity commitments, and financial data into a common planning framework.
- Reconcile model outputs to actuals and implement data-quality controls for missing tags, anomalies, and duplicate records.
- Build forecasting and scenario-analysis tools that help leaders evaluate capacity, utilization, and pricing decisions before committing spend.
- Identify optimization opportunities such as idle reserved capacity, underutilized clusters, and inefficient workloads, and quantify the associated savings.
- Automate recurring capacity-planning, forecasting, and reporting workflows.
- Partner with engineering teams to understand workload growth, migrations, SLOs, and architecture changes that affect capacity needs.
- Work with CIBO, Finance, and Procurement to support cloud commitments, infrastructure investment decisions, and long-range capacity planning.
- Communicate insights, risks, and recommendations clearly to technical and business stakeholders.
Minimum Qualifications
- 3+ years of experience in Data Engineering, Analytics Engineering, Infrastructure Analytics, or a related field.
- Strong SQL skills and experience working with large datasets.
- Experience with Python or another language used for data processing and automation.
- Experience building data pipelines, data models, and analytical datasets.
- Understanding of ETL/ELT patterns, data quality, and pipeline reliability.
- Experience working with cloud billing and usage data from AWS, GCP or Azure
- Proven ability to build data models that reconcile to a financial source of truth
- Understanding of AI and ML inference workloads and how model serving drives compute cost
- Strong analytical and problem-solving skills.
- Ability to work effectively with both technical and non-technical partners.
- Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Economics, Finance, or a related quantitative field, or equivalent practical experience.
Preferred Qualifications
- Experience with infrastructure capacity planning, forecasting, or resource-management data.
- Experience working with GPU, TPU, CPU, storage, or cloud infrastructure.
- Experience with AWS, GCP, or similar cloud platforms.
- Understanding of AI/ML infrastructure and accelerator utilization.
- Experience with infrastructure cost, billing, or utilization datasets.
- Experience with technologies such as Spark, Trino, Airflow, Kafka, or similar data-platform tools.
- Experience with Tableau or other visualization platforms.
- Familiarity with infrastructure economics, cloud commitments, or capacity optimization.
- Experience partnering with Engineering, Finance, or Procurement on infrastructure planning.