Senior Software Engineer
19 days ago London, United Kingdom
About us
Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.
- Size: 10,000+ employees
- Industry: Technology: Manufacturing
Why you should apply for a job to Apple:
Ranked as one of the Best Technology Companies in 2020
3.7/5 in overall job satisfaction
3.1/5 in supportive management
66% say women are treated fairly and equally to men
65% would recommend this company to other women
66% say the CEO supports gender diversity
Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›
Do you enjoy crafting elegant solutions to distributed problems with billions of data points per day? Can you manage business complexity and still deliver reliable, scalable systems? If so, join us! You will work with engineers across Apple to build data pipelines and real-time streaming applications that power Apple Music, Shazam, Apple Podcasts and Fitness+ services.
Description
We build big-data and real-time data platform components and pipelines. We are domain experts who work alongside product, business and client engineering teams. AI is already a core part of how we work. We have access to leading frontier tools and use agentic engineering to accelerate our development, operations and support work. Our data supports a modern AI stack powering features and recommendations.","responsibilities":"Build at scale: Design, build and evolve distributed compute frameworks, data pipelines and real-time streaming applications that process billions of data points per day.
Turn questions into specs: Work with partners to understand what they need and why. Write clear technical specs that define datasets, metrics and requirements, and review specs written by others.
Deliver with partners: Work on cross-functional projects with engineering teams, product leads, data scientists and analytics leaders to build datasets, metrics and insights.
Drive technical direction: Lead initiatives from problem discovery through to production. Weigh cost, complexity and performance, and choose the right tool for each problem.
Run it well: Share ownership of high-throughput, low-latency production systems. That includes on-call, and hardening systems against failure before it happens.
Raise the bar: Do thorough code and design reviews, mentor other engineers, and champion testing and engineering best practices.
Preferred Qualifications
Practical experience maintaining large-scale data pipelines with Spark and Flink.
Experience with cloud, container and infrastructure tools such as Kubernetes and Docker.
Effective use of AI tools such as Claude in day-to-day engineering.
Contributions to open-source data-processing projects.
Excellent written and verbal communication across distributed teams.
Bachelor's or Master's degree in Computer Science, Software Engineering or equivalent experience.
Minimum Qualifications
Substantial experience designing, building and operating business-critical, large-scale distributed systems.
Fluency in at least one statically typed language, ideally Java or Scala, and a strong grasp of big-data fundamentals: concurrency, memory management, networking and performance optimisation.
In-depth understanding of at least one distributed or big-data system, such as Kafka, Flink, Spark, Trino, Iceberg or Cassandra.
Experience gathering requirements and turning ambiguous business needs into clear, accurate technical specifications.
A focus on quality and craftsmanship: You reason well about complex failure modes and deliver well-tested, maintainable code.
Description
We build big-data and real-time data platform components and pipelines. We are domain experts who work alongside product, business and client engineering teams. AI is already a core part of how we work. We have access to leading frontier tools and use agentic engineering to accelerate our development, operations and support work. Our data supports a modern AI stack powering features and recommendations.","responsibilities":"Build at scale: Design, build and evolve distributed compute frameworks, data pipelines and real-time streaming applications that process billions of data points per day.
Turn questions into specs: Work with partners to understand what they need and why. Write clear technical specs that define datasets, metrics and requirements, and review specs written by others.
Deliver with partners: Work on cross-functional projects with engineering teams, product leads, data scientists and analytics leaders to build datasets, metrics and insights.
Drive technical direction: Lead initiatives from problem discovery through to production. Weigh cost, complexity and performance, and choose the right tool for each problem.
Run it well: Share ownership of high-throughput, low-latency production systems. That includes on-call, and hardening systems against failure before it happens.
Raise the bar: Do thorough code and design reviews, mentor other engineers, and champion testing and engineering best practices.
Preferred Qualifications
Practical experience maintaining large-scale data pipelines with Spark and Flink.
Experience with cloud, container and infrastructure tools such as Kubernetes and Docker.
Effective use of AI tools such as Claude in day-to-day engineering.
Contributions to open-source data-processing projects.
Excellent written and verbal communication across distributed teams.
Bachelor's or Master's degree in Computer Science, Software Engineering or equivalent experience.
Minimum Qualifications
Substantial experience designing, building and operating business-critical, large-scale distributed systems.
Fluency in at least one statically typed language, ideally Java or Scala, and a strong grasp of big-data fundamentals: concurrency, memory management, networking and performance optimisation.
In-depth understanding of at least one distributed or big-data system, such as Kafka, Flink, Spark, Trino, Iceberg or Cassandra.
Experience gathering requirements and turning ambiguous business needs into clear, accurate technical specifications.
A focus on quality and craftsmanship: You reason well about complex failure modes and deliver well-tested, maintainable code.
Additional Information
- Posted: 19 days ago