Data Engineer, Amazon Music, Amazon Music Finance
Posted Updated
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. Learn more at https://www.amazon.com/music.
The Data, Insights, Science and Optimization, Finance (DISCO Finance) team is looking for a Data Engineer to join a team of Data Scientists, Business Intelligence Engineers, and Data Engineers who analyze data at scale and build the models and algorithms that power the Music product experience. DISCO Finance accelerates Amazon Music customer growth by empowering Product teams to make customer-centric decisions through data and insights. We build the data pipelines, self-service analytics, and predictive models that enable acquisition, engagement, and retention at scale.
In this role, you will design, build, and own the data infrastructure and pipelines that power the team's analytics and science, and partner with stakeholders across marketing, growth, product, science, and finance to scale those capabilities. The ideal candidate builds reliable large-scale data solutions, prioritizes across competing stakeholders and projects, and thrives in a fast-paced, dynamic environment.
Key job responsibilities
• Engage in collaborative efforts with cross-functional teams — data scientists, business intelligence engineers, and Finance Managers — to architect a state-of-the-art data analytics platform on AWS using the AWS Cloud Development Kit (CDK).
• Construct resilient and scalable data pipelines using SQL/PySpark/Airflow to ingest, process, and transform substantial data volumes from diverse sources into a structured format, ensuring data quality and integrity.
• Devise and implement an efficient, scalable data warehousing solution on AWS, utilizing appropriate NoSQL/SQL storage and database technologies for both structured and unstructured data.
• Automate ETL/ELT processes to streamline data integration from diverse sources, enhancing the platform's reliability and efficiency.
• Develop data models to support business intelligence, delivering actionable insights and interactive reports to end-users.
• Enable advanced analytics, machine learning, and generative AI capabilities within the platform, extracting predictive and prescriptive insights through tools like EMR and SageMaker.
• Continuously monitor and optimize the performance of data pipelines, databases, and applications, ensuring low-latency data access for analytics and machine learning tasks.
• Implement robust security measures and ensure data compliance with internal requirements, industry standards, and regulations to safeguard sensitive information.
• Collaborate closely with data scientists, business intelligence engineers, and Finance Managers to understand their requirements and partner on data projects.
• Generate comprehensive technical documentation covering the platform's architecture, data models, and APIs, promoting knowledge sharing and ease of maintainability.
The Data, Insights, Science and Optimization, Finance (DISCO Finance) team is looking for a Data Engineer to join a team of Data Scientists, Business Intelligence Engineers, and Data Engineers who analyze data at scale and build the models and algorithms that power the Music product experience. DISCO Finance accelerates Amazon Music customer growth by empowering Product teams to make customer-centric decisions through data and insights. We build the data pipelines, self-service analytics, and predictive models that enable acquisition, engagement, and retention at scale.
In this role, you will design, build, and own the data infrastructure and pipelines that power the team's analytics and science, and partner with stakeholders across marketing, growth, product, science, and finance to scale those capabilities. The ideal candidate builds reliable large-scale data solutions, prioritizes across competing stakeholders and projects, and thrives in a fast-paced, dynamic environment.
Key job responsibilities
• Engage in collaborative efforts with cross-functional teams — data scientists, business intelligence engineers, and Finance Managers — to architect a state-of-the-art data analytics platform on AWS using the AWS Cloud Development Kit (CDK).
• Construct resilient and scalable data pipelines using SQL/PySpark/Airflow to ingest, process, and transform substantial data volumes from diverse sources into a structured format, ensuring data quality and integrity.
• Devise and implement an efficient, scalable data warehousing solution on AWS, utilizing appropriate NoSQL/SQL storage and database technologies for both structured and unstructured data.
• Automate ETL/ELT processes to streamline data integration from diverse sources, enhancing the platform's reliability and efficiency.
• Develop data models to support business intelligence, delivering actionable insights and interactive reports to end-users.
• Enable advanced analytics, machine learning, and generative AI capabilities within the platform, extracting predictive and prescriptive insights through tools like EMR and SageMaker.
• Continuously monitor and optimize the performance of data pipelines, databases, and applications, ensuring low-latency data access for analytics and machine learning tasks.
• Implement robust security measures and ensure data compliance with internal requirements, industry standards, and regulations to safeguard sensitive information.
• Collaborate closely with data scientists, business intelligence engineers, and Finance Managers to understand their requirements and partner on data projects.
• Generate comprehensive technical documentation covering the platform's architecture, data models, and APIs, promoting knowledge sharing and ease of maintainability.