Data Engineer, Amazon Music, Amazon Music Finance

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. 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

• Partner with cross-functional teams — data scientists and Finance managers — to architect a modern data analytics platform on AWS using the Cloud Development Kit (CDK).
• Build resilient, scalable data pipelines with SQL, PySpark, and Airflow to ingest, process, and transform large data volumes from diverse sources into structured, high-quality datasets.
• Design and implement a scalable data warehousing solution on AWS, using the appropriate NoSQL and SQL storage and database technologies for structured and unstructured data.
• Build a consolidated data model that serves as the single source of truth, so metric logic is defined and changed once rather than patched across disconnected jobs.
• Automate ETL and ELT processes to streamline data integration across sources and improve platform reliability and efficiency.

Own the business intelligence layer end to end

• build the data models, metrics, dashboards, and interactive reports that deliver actionable insights to Finance managers and other end users.
• Build AI and generative AI infrastructure that automates recurring Finance reporting and process work, using tools such as EMR and SageMaker to extract predictive and prescriptive insights.
• Build AI agents and agentic infrastructure that let Finance managers self-serve their questions and analytics, reducing ad hoc reporting requests.
• Build KPI and ETL job-monitoring systems with freshness SLAs, anomaly detection, and lineage tracking, so business-critical reports arrive on time and data access stays low-latency for analytics and machine learning.
• Partner with the central infrastructure team to drive infrastructure enhancements for Finance-specific use cases.
• Implement robust security measures and ensure data compliance with internal requirements, industry standards, and regulations to protect sensitive information.
• Work closely with data scientists and Finance managers to understand their reporting and analytics requirements and translate them into platform capabilities.
• Produce clear technical documentation covering the platform's architecture, data models, and APIs to support knowledge sharing and maintainability.