We are seeking a hardworking, performance-savvy, engineer to build out the big data platform and services, which power many of these customer features — existing and new. As a core member of the Data Engineering team you will be responsible for designing and implementing features that rely on processing and serving very large datasets with an awareness of scalability. This will include crafting systems to model, ingest, process and compute large-scale, mission-critical data across Apple Music. High-throughput and reliability are essential. The computed datasets are produced for internal reporting used by executives and they are also shared across many teams within the ASE organization, such as the Search team, Recommendation team, AB team, Marketing team. You will also 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. This is your opportunity to help engineer highly visible global-scale systems with petabytes of data, supporting hundreds of millions of users. Come join us to help deliver the next amazing Apple product!
Bachelor’s degree in Engineering, Computer Science, Business Information Systems, or related field
At least 5+ years relevant industry experience
Experience with distributed computing technologies such as Hadoop and Spark
Proficiency in Scala, Java and SQL
Expertise in designing, implementing and supporting highly scalable data systems and services
Expertise building and running large-scale data pipelines, including distributed messaging such as Kafka, data ingest from various sources to feed batch and near-realtime or streaming compute components
Solid understanding of data-modeling and data-architecture optimized for big data patterns, such as efficient storage and query on HDFS
Experience with distributed storage and network resources, at the level of hosts, clusters and DCs, to troubleshoot and prevent performance issues
Experience with data lake and data warehouse solutions