Job 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. 3+ years of software development experience Experience in at least one of the following: Spark, Kafka and Web Services 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 Designing Java-based micro-services using best practices and open-source libraries 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

Requirements

Must-haves

3+ years of software development experience
Experience in at least one of the following: Spark, Kafka and Web Services
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
Designing Java-based micro-services using best practices and open-source libraries
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

javasqlawsdockerkubernetes

Platform: apple
Effort: medium