Job Description
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale.
BASIC QUALIFICATIONS
– 1+ years of data engineering experience
– Experience with SQL
– Experience with data modeling, warehousing and building ETL pipelines
– Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
– Experience with one or more scripting language (e.g., Python, KornShell)
PREFERRED QUALIFICATIONS
– Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
– Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
Key job responsibilities
Build Data Platform and Data Lake solutions
Build Data Engineering tools
Build real time and micro batch data pipelines
About the team:
The Music Data eXperience (MDX) team is responsible for the definition, design, production, and quality of foundational datasets consumed by the whole org, data management tools, and the self-service data lake and warehouse platforms on which these datasets are published, stored, shared, and consumed for analytics and science modeling. MDX is split into two sub teams *PARAM* (Platform Architecture Research and AutoMation) and *IDEA* (Intelligence, Data Engineering & Analytics). Data Platform (PARAM) team owns the self-service data lake Data EXchange Store (DEX) and Data Warehouse platforms, builds tools and frameworks for efficient data management, and owns the orchestration and configuration platform for data pipelines. Data Engineering (IDEA) Team owns the foundational data model and datasets, the Spark and Datanet ETL jobs and business logic to build them, away team support for datasets, org wide launch support (when required), the Executive Daily Summary (EDS), and future batch dataset data quality frameworks.
