Data Engineer - Capacity Planning - Apple Data Platform - Jobs - Careers at Apple
- 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.
- 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.
- 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.
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Required Skills
Required Languages
🇬🇧 English