The AWS Sales Marketing Global Services Ops (SMGS Ops) Revenue team publishes mission-critical Sales Revenue data product including, Unified Estimated Revenue, GAAP Sales Revenue, Quota Sales Retirement, and Planning Revenue that power strategic decision-making across AWS Finance, Accounting, Sales Operations, Segmentation Planning & Policy (S&P), Global Sales Compensation, Field Experience, and Data & Analytics teams.
Our platform processes billions of metered usage and billing transactions daily, integrating data from 20+ sources including Salesforce.com to deliver accurate, timely revenue insights that drive quota setting, sales goal management, forecasting, compensation, and business intelligence. We leverage BigData technologies including Elastic Map Reduce (EMR), Spark, Redshift, and Glue and other AWS services to handle massive data volumes at scale.
The Opportunity
We're seeking a passionate Data Engineer to architect and extend our processing framework for next-generation revenue data products. In this high-impact role, you'll:
• Build scalable data pipelines that seamlessly handle exponential data growth and enable rapid development of new revenue products
• Lead AI-driven automation initiatives to transform revenue processing from data acquisition and validation to publishing and insights generation into intelligent, systematic workflows
• Architect solutions that enhance data quality, reduce processing latency, and deliver actionable insights to stakeholders across the organization
This role places you at the forefront of innovation, combining big data engineering with AI/ML to revolutionize how AWS processes and reports revenue at global scale.
Key job responsibilities
• Evolve the Revenue technology stack to support AWS's rapid expansion while maintaining compliance with security policies, architectural standards, and operational best practices
• Design and build end-to-end data pipelines that transform raw data from diverse upstream sources into actionable business insights powering quota management, forecasting, and compensation decisions
• Develop frameworks, tools, and AI-driven solutions that eliminate manual processes, reduce operational overhead, and accelerate time-to-insight for Revenue customers
• Create and maintain dimensional data models and data structures that enable self-service analytics and support evolving reporting requirements across Finance, Sales Operations, and Analytics teams
• Mentor a team of Data Engineers to deliver cross-functional solutions, foster technical growth, and establish engineering excellence standards
• Architect and optimize Revenue data infrastructure. Ensure enterprise-grade security, cost efficiency, and scalability while processing billions of daily transactions across 20+ data sources
• Collaborate with product managers, stakeholders, and customers to translate business requirements into technical solutions
BASIC QUALIFICATIONS
- 3+ years of data engineering experience
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
- Bachelor's degree or above in Computer Science, Computer Engineering, Data Science, Electrical Engineering, or majors relating to these fields, or 3+ years of professional software development experience
- Experience in at least one object-oriented programming language (e.g., JavaScript, Python, C#)
PREFERRED QUALIFICATIONS
- 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)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
