Senior / Staff Software Engineer (Flink, Beam, Kafka, Iceberg), Apple Data Platform

Seeking extraordinary software engineers with deep experience in scalable streaming processing systems. You will have a passion to push the limits of distributed stateful streaming computing frameworks to get every ounce of performance out of them. Looking for engineers with in depth knowledge of systems like Flink, Beam, Kafka, Iceberg and other existing frameworks. Also someone who will be passionate by the prospect of working reciprocally with other groups internal to Apple and also communities outside Apple.

As an engineer on our team, you will advance our data infrastructure, driving innovation by implementing cutting-edge capabilities and tools that will power Apple Services Engineering. You’ll collaborate closely with a dedicated team of open source data experts (including Spark, Flink, Kafka, Iceberg committers amongst others) to enhance and automate our infrastructure, ensuring seamless management across both data centers and cloud services. Your work will directly contribute to the resilience, scalability, and performance of the systems that support millions of users worldwide.

What you can expect to do and contribute to:

• Develop and deploy new features of Apple’s internal data platform.
• Regularly contribute to open source.
• Collaborate with cross-functional teams to design and implement automation tools that streamline operations and reduce manual intervention.
• Optimize our cloud-based services to ensure they scale effectively, handling increasing loads while maintaining high performance.
• Innovate on API development and integration, enabling seamless communication between our applications and services
• This is an opportunity to join a dynamic and evolving team where your contributions will shape the future of our Apple’s technology landscape.

Key Qualifications

• 8+ years of professional experience professional experience.
• A successful track-record or demonstrated aptitude as an engineer who has worked on distributed systems.
• Experienced Poweruser (at least 2 years of running production applications) of Apache Spark or Committer to Apache Spark.
• BS or MS degree in computer science or equivalent

Preferred Qualifications

• Good knowledge of Apache Iceberg and Apache Kafka.
• Experience working on or building connectors from Spark to any of the data sources.
• Knowledge of Yarn, Kubernetes or other compute substrate.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $171,600 and $302,200, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.