Data Engineer - Analytics Engineering

Summary

Apple Services Engineering (ASE) is the organisation responsible for products such as Apple Music, Podcasts, TV+, tvOS, App Store, iCloud, and many others.nnWe, at ASE Analytics and Data Engineering, are responsible for collecting, analysing and reporting on insights derived from user and device generated data from across all Apple media services.nnReporting plays a crucial role in this process, enabling teams at Apple to gain valuable insights and make informed decisions about their daily activities. Reporting involves integrating data from multiple data pipelines managed by different teams, which presents challenges such as achieving clear visibility into the dependencies and SLAs of the contributing flows. To address these challenges, we are establishing a new team in Dublin to develop internal tools that will enhance our ability to manage these complexities.

Description

We are looking for a Data Engineer to join our Analytics u0026 Data Engineering Knowledge graph team, designing and develop data pipelines. Our knowledge graph aims to unify insights on data processing, data lineage, and data infrastructure across the entire Services division, to generate a rich operational health view of our data pipelines. The graph will power operational excellence, driving incident analysis, guide agentic resolution of issues, and enabling proactive avoidance of future incidents. nnYou’ll work alongside a Dublin based team of software and other data engineers committed to bringing the knowledge graph to life. You will have significant individual responsibility and influence over the direction of this critical service, and the opportunity to learn and interact with a department of global teams each with unique skill sets and operating in different time zones.

Minimum Qualifications

Bachelor’s degree in a scientific field (Computer Science, Computer Engineering, Mathematics preferred)nIndustry experience in a Data EngineeringnExperience working with Spark and other distributed data technologies (e.g. Hadoop) for building efficient u0026 large scale data pipelines.

Preferred Qualifications

Growth mindset and ability to learn new technologiesnGood understanding of software development life cycle, version control, code reviews, testing, data quality tools and frameworksnFamiliar with Data Lake and Data Warehouse technologiesnExperience with job orchestrators and how they work (eg Airflow)nExperience with streaming data pipelines (eg Kafka, Flink, Spark-Streaming)nExperience with a graph database (eg Neo4j, ArangoDB, TigerGraph)