Apple Music is 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.
Requirements
- 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
Responsibilities
- designing and implementing features that rely on processing and serving very large datasets with an awareness of scalability
- 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
- help engineer highly visible global-scale systems with petabytes of data, supporting hundreds of millions of users
- help deliver the next amazing Apple product!
Other
- At least 5+ years relevant industry experience
- Experience with Apache Flink
- Experience with Apache Iceberg tables
- Experience with Apache Beam
- Familiarity with Docker and Kubernetes
- Familiarity with Apache Airflow
