This role pays less than
58%
of similar roles. Most pay
$143,625–$223,750
— the shaded band above.
At the midpoint, this role pays about
$177k
versus about
$184k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3587 open roles
on FindRole.
Listed pay typically runs
$165,800–$277,600
across 2768 roles with salary data.
Data Engineer - Capacity Planning - Apple Data Platform joins the Apple Data Platform team to build a data foundation for capacity planning across data and AI infrastructure. You will develop data pipelines, models, and analytical tools to analyze infrastructure demand, utilization, capacity, and cost. The role focuses on third-party cloud infrastructure including GPUs, TPUs, compute, and storage. Day-to-day responsibilities include integrating telemetry, workload demand, and financial data into trusted datasets, building cost models for unit economics, and creating forecasting tools for investment decisions. You will utilize SQL, Python, Spark, Trino, Airflow, Kafka, and Tableau to automate workflows and ensure data quality. The role addresses the complex problem of managing infrastructure efficiency and making informed planning decisions regarding cloud commitments and AI/ML inference workloads while collaborating with engineering, finance, and procurement teams.
What does a Data Engineer earn in California?
Median $187556 from 35 postings across 19 companies.
Build and maintain data pipelines for infrastructure capacity, utilization, performance, and cost data.
Develop trusted data models for GPU, TPU, CPU, and storage resources.
Create cost models to calculate unit economics like cost per GPU hour or cost per million tokens.
Integrate workload demand, telemetry, and financial data into a common planning framework.
Implement data-quality controls to reconcile model outputs with actual records and identify anomalies.
Build forecasting and scenario-analysis tools to evaluate capacity and pricing decisions before spending.
Identify and quantify optimization opportunities for idle capacity and underutilized clusters.
Automate recurring reporting workflows for capacity planning and infrastructure forecasting.
What we're looking for
3+ years of experience in Data Engineering, Analytics Engineering, Infrastructure Analytics, or a related field.
Bachelor's degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Economics, Finance, or a related quantitative field, or equivalent practical experience.
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.
Experience with infrastructure capacity planning, forecasting, or resource-management data (preferred).
Experience working with GPU, TPU, CPU, storage, or cloud infrastructure (preferred).
Experience with technologies such as Spark, Trino, Airflow, Kafka, or similar data-platform tools (preferred).
Experience with Tableau or other visualization platforms (preferred).
Kubernetes
Go
Python
Java
AWS
GCP
Ali Cloud
Linux
Flink
Hive
Hadoop
HDFS
Trino
Druid
Containers
Virtualization
Site Reliability Engineering
Distributed Systems