Streaming Platform Engineering Lead — Scale Data Infra & Kafka

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

Benefits offered by this job

Comprehensive medical and dental cover

Retirement benefits

Employee stock programs

Tuition reimbursement

Job summary

Apple, Inc. is seeking an experienced Software Engineering Manager to lead the Streaming Platform and Solutions team in Seattle. You will shape and deliver high-scale data infrastructure powering Apple Music, TV, and Podcasts, mentoring engineers and steering architecture across regions.

You will directly influence operational excellence, latency-sensitive data flows, and cross-datacenter replication strategies while partnering with product and data teams to meet ambitious goals.

Qualifications

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

Responsibilities

• Lead, mentor, and grow a team of software engineers with a culture of rigor.
• Write code, review PRs, and drive architectural decisions for scalable systems.
• Coordinate with regional leaders to align roadmaps across regions.
• Oversee architectural reviews, incident management, and performance tuning.
• Guide engineers to balance execution with work–life balance.

Skills

Distributed systems

Leadership

Java/Scala

Algorithms & data structures

Practical experience with CS research

Education

BS/MS/PhD in Computer Science

Tools

Apache Kafka

Kafka internals

Job description

Apple, Inc. is seeking an experienced Software Engineering Manager to lead the Streaming Platform and Solutions team in Seattle. You will shape and deliver high-scale data infrastructure powering Apple Music, TV, and Podcasts, mentoring engineers and steering architecture across regions.

You will directly influence operational excellence, latency-sensitive data flows, and cross-datacenter replication strategies while partnering with product and data teams to meet ambitious goals.