Engineering Manager — Streaming Platform & Kafka

Apple Inc.; Seattle (WA); On-site; USD 196,000 - 338,000; Full time

Benefits offered by this job

Medical & dental coverage

Employee stock programs

Relocation assistance

Tuition reimbursement

Job summary

Apple Streaming Platform and Solutions in Seattle, WA seeks an experienced Software Engineering Manager to lead a team within Data Services responsible for distributed data systems powering Apple Music, TV, and Podcasts. You will mentor engineers and shape technical strategy.

You’ll work on real-time streaming, cross-datacenter replication, and low-latency data delivery at global scale, with emphasis on Kafka, performance, and reliability.

Qualifications

• 5+ years in software development, with 3+ years in distributed systems or messaging at scale.
• 2+ years in technical leadership overseeing engineers and projects.
• Proven track record leading infrastructure teams with credibility.
• Proficiency in Java, Scala, or Kafka-related languages.
• Strong understanding of algorithms, data structures, fault tolerance, and performance for large-scale messaging.

Responsibilities

• Lead, mentor, and grow a team of software engineers; drive technical skill development.
• Stay hands-on with code, PR reviews, and architectural trade-offs; ensure best practices.
• Coordinate with regional and cross-functional teams to align objectives and roadmaps.
• Ensure high availability, low latency, and operational excellence across platform services.

Skills

Distributed systems

Technical leadership

Java/Scala

Algorithms & data structures

Kafka

Education

BS/MS/PhD in CS or related field

Tools

Apache Kafka

Kafka internals

Job description

Apple Streaming Platform and Solutions in Seattle, WA seeks an experienced Software Engineering Manager to lead a team within Data Services responsible for distributed data systems powering Apple Music, TV, and Podcasts. You will mentor engineers and shape technical strategy.

You’ll work on real-time streaming, cross-datacenter replication, and low-latency data delivery at global scale, with emphasis on Kafka, performance, and reliability.