Apple Media Products is the team behind the App Store, Apple Music, iTunes, and many other high profile products on iPhone, Mac and AppleTV. Our Data Engineering team is looking for talented, performance-savvy, engineers to build out the big data platform and services which power many of these customer features — existing and new. This is your opportunity to help engineer highly visible global-scale systems with petabytes of data, supporting hundreds of millions of users.
You will be responsible for designing and implementing features that rely on processing and serving very large datasets, so an awareness of scalability is required. This will include creating systems to model, ingest, process and compute large-scale, mission-critical data across Apple Music. High-throughput and reliability are critical.
You will enjoy the benefits of working in a fast growing business where you are encouraged to “Think Different” to solve very interesting technical challenges and where your efforts play a key role in the success of Apple’s business.
You should be able to engineer innovative solutions while playing a hands-on development role to deliver products in a dynamic environment. Leadership is important for this position as you will be asked to provide technical guidance and best practices.
You will have to interact with other groups on an ongoing basis on both technical and non-technical levels. Good verbal and written communication skills are important to this position.
- Significant experience in designing, implementing and supporting highly scalable data systems and services in Java and/or Scala
- Bachelors/equivalent, or greater, in Computer Science or related discipline
- Experience in Hadoop/Spark and Kafka preferred
- Experience of any of the following is an advantage:
- Hadoop-ecosystem technologies in particular MapReduce, Spark / Spark-SQL / Spark Streaming, Hive, YARN/MR2
- Development of high-throughput, secure web-services, with REST, in particular DropWizard, Jetty, vertx.io
- Experience with low-latency NoSQL datastores and traditional relational databases is a plus, in particular Druid, Cassandra, Voldemort or HBase
- Building and running large-scale data pipelines, including distributed messaging such as Kafka, data ingest to/from multiple sources to feed batch and near-realtime/streaming compute components
- Knowledgable about distributed storage and network resources, at the level of hosts, clusters and DCs, to troubleshoot and prevent performance issues
- Data-modeling and data-architecture optimized for big data patterns (warehousing concepts; efficient storage and query on HDFS; data security and privacy techniques)
BS in Computer Science or equivalent, MS preferred.