Data Iceberg Engineer Jobs

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Data Engineer

Apple Cupertino, CA

Cupertino, CA

Today

$129k - $195k/yr Full time Artificial Intelligence Big Data Bigdata

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Job Description

Apple’s Data Org is looking for a Data Engineer to support Apple Ads, where privacy-focused data engineering and machine learning systems power advertising capabilities across real-time, near-real-time, and batch workflows. In this onsite role in Cupertino, you will design and deliver scalable data and ML components, improve reliability and performance, and use AI tools to move faster while validating correctness, privacy, and cost.

This position is based in Cupertino, CA (onsite), with a yearly salary range of USD 129,300 - 194,700. You will work within sprint-based Agile teams alongside engineers and product managers to build reusable, efficient infrastructure for mission-critical systems.

Responsibilities

Engineer secure, scalable data and machine learning systems across real-time, near-real-time, and batch execution using tools such as Spark, Kafka, and Iceberg, and related technologies
Own design and delivery of core components, spanning pipeline architecture and ML model development, training, and deployment, including privacy-preserving infrastructure
Improve reliability, performance, and efficiency, including supporting schema changes, backfills, and experimentation and testing infrastructure such as A/B testing
Apply a strong understanding of the intersection between business, analytics, and engineering, with an emphasis on reusable and efficient solutions
Use LLMs and AI coding agents (for example, Claude and Gemini) daily for implementation, testing, and debugging, while continually validating results for correctness, privacy, and cost
Collaborate with a team of engineers and product managers, growing through code and design reviews and mentoring others as seniority increases
Contribute to on-call, monitoring, and ongoing reliability and efficiency improvements, with more senior engineers supporting incident response and root-cause analysis
Operate effectively in a rapidly changing, sprint-based Agile environment, contributing to a culture focused on reliability, resiliency, extensibility, scalability, and productivity

Requirements

1+ years of industry experience building scalable data pipelines and machine learning systems, or other distributed software at scale
Strong computer science and software engineering fundamentals
Proficiency in modern programming languages such as Rust, Python, Java, or Scala
Experience with distributed systems and data processing technologies, including Spark, Kafka, or Flink
Experience building and scaling systems on premise and in the cloud
Solid understanding of data structures, algorithms, and system design principles
Ability to communicate effectively with cross-functional technical and non-technical teams
Hands-on experience using LLMs (for example, Claude and Gemini) in daily engineering work for tasks such as code generation, review, debugging, test writing, agentic loops, and evaluation systems, to improve skills and velocity
Excellent collaborative skills
BS/MS in Computer Science, Software Engineering, Distributed Systems, or a related field

Technologies

Spark
Kafka
Iceberg
Flink
Rust
Python
Java
Scala
LLMs
Claude
Gemini
A/B testing

Benefits

Comprehensive medical and dental coverage
Retirement benefits
Discounted products and free services
Reimbursement for certain educational expenses, including tuition
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 if voluntarily participating in Apple’s Employee Stock Purchase Plan
Discretionary bonuses or commission payments (as applicable)
Relocation (as applicable)

Preferred Qualifications

Experience with NoSQL datastores (e.g. Cassandra, Keyspaces, ElastiCache)
Experience with lakehouse and Iceberg table formats
Experience with anomaly detection
Experience with A/B experimentation frameworks
History of driving reliability, efficiency, or cost improvements, and mentoring other engineers
Comfortable working in a rapidly changing environment with ambiguous requirements
Prior experience in the advertising industry (a huge plus)

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