Data Engineer - Capacity Planning

Apple is seeking a Data Engineer for Capacity Planning to design and scale the data foundation used to forecast infrastructure demand and associated costs. In this onsite role in Cupertino, you will start by supporting third-party cloud infrastructure capacity, including GPUs, TPUs, compute, and storage, with a roadmap to expand coverage to Apple-owned infrastructure over time.

You will build pipelines, trusted datasets, and analytical tooling that connect operational utilization telemetry with workload demand and financial reporting. The goal is to help leaders evaluate capacity, utilization, and pricing decisions before committing spend, while continuously improving data quality and model accuracy.

What you’ll do

• Build and maintain data pipelines covering infrastructure capacity, utilization, performance, and cost datasets.
• Develop trusted data models for GPU, TPU, CPU, storage, and other infrastructure resources.
• Create cost models that compute unit economics such as cost per GPU hour, cost per job, and cost per 1M tokens using measured production utilization.
• Bring together workload demand, utilization telemetry, capacity commitments, and financial data into a shared planning framework.
• Reconcile model outputs to actuals, and add data-quality controls for missing tags, anomalies, and duplicate records.
• Build forecasting and scenario-analysis tools to support decisions on capacity, utilization, and pricing prior to infrastructure commitments.
• Identify optimization opportunities, including idle reserved capacity, underutilized clusters, and inefficient workloads, and quantify the expected savings.
• Automate recurring capacity-planning, forecasting, and reporting workflows.
• Partner with engineering teams to understand workload growth, migrations, SLOs, and architecture changes that impact 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.

Technologies

• SQL, Python
• ETL/ELT
• AWS, GCP, Azure
• Spark, Trino, Airflow
• Kafka, Tableau

Compensation and location

• Location: Cupertino, CA (onsite)
• Salary: USD 129,300 - 225,300 per year

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Range of discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Discretionary bonuses or commission payments as well as relocation
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Discretionary restricted stock unit awards
• Purchase Apple stock at a discount through voluntary participation in the Employee Stock Purchase Plan
• Eligibility requirements and other terms of the applicable plan or program

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.