Senior Software Engineer, Apple Services Engineering - Music Data Engineering

Imagine what you could do here. The people here at Apple don’t just build products — we craft the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that supports the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. This team is more than a group of engineers -- it's a group passionate about Apple Music and related products. Apple Music is the world’s most complete music experience, with over 60 million songs, thousands of playlists, and daily selections from music experts for 115 countries. The team’s data-driven engineers focus relentlessly on the customer experience by running worldwide experiments and analyzing usage and latency, while collaborating with Apple’s product groups. As a result, you can share your favorite album from Apple Music with your friends, while enjoying access to Photos, Arcade, Apple TV+ and more, all working seamlessly together, all made just for you.

Description

As a member of the Music Data Engineering team, you will build data processing pipelines and services to power many of the customer features in Apple Music. You will design complex and highly scalable applications to model, ingest, compute and serve large-scale, mission-critical data. You will collaborate with technical staff across engineering, research, product and business teams to find the best solutions and create innovative products, all in support of our customers' experience of Apple Music.

Minimum Qualifications

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

Preferred Qualifications

Experience in Spark, Kafka and Web Services preferred (see below) Designing Java-based micro-services using best practices and open-source libraries including DropWizard, Sprint, Large-scale data processing technologies in particular Spark / Spark-SQL / Spark Streaming, Flink, Hadoop, Object-Stores, AWS Lambda or similar 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 Experience with any low-latency NoSQL datastores, including Cassandra and S3 Familiarity with Docker, Kubernetes, Maven, Gradle, Git and other related software lifecycle tools