Data Engineer, Amazon Music, Amazon Music Finance

Amazon is an immersive audio entertainment company offering music, podcasts, livestreams, and other experiences through Amazon Music. The Data Engineer will design, build, and own data infrastructure, pipelines, analytics platforms, data warehouses, and AI capabilities that support Finance, product, marketing, growth, and science teams.

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

Preferred

• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
• AWS
• Amazon Redshift
• Amazon S3
• AWS Glue
• Amazon EMR
• Amazon Kinesis
• Amazon Data Firehose
• AWS Lambda
• IAM Roles
• Permissions
• Non-Relational Databases
• Object Storage
• Document Stores
• Key-Value Stores
• Graph Databases
• Column-Family Databases

Benefits

• Sign-on payments
• Restricted stock units (RSUs)
• Health insurance, including medical, dental, vision, and prescription coverage
• Basic Life & AD&D insurance
• Option for Supplemental life plans
• Employee Assistance Program (EAP)
• Mental Health Support
• Medical Advice Line
• Flexible Spending Accounts
• Adoption and Surrogacy Reimbursement coverage
• 401(k) matching
• Paid time off
• Parental leave

Company

• Online retail and cloud computing services
• Seattle, Washington, USA
• https://amazon.com/
• Founded in 1994
• 10001+ employees

Qualifications

• 3+ years of data engineering experience
• Experience with data modeling, warehousing and building ETL pipelines
• Bachelor's degree
• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

Required skills

• Data Engineering, Data Modeling, Data Warehousing, ETL Pipeline Development, SQL, PySpark, Apache Airflow, AWS, AWS CDK, Amazon Redshift, Amazon S3, AWS Glue, Amazon EMR, Amazon Kinesis, Amazon Kinesis Data Firehose, AWS Lambda, AWS IAM, Non-relational Databases

Experience

• 3+ years