Data Engineer II

Data Engineer II on AWS AI Services' Data Engineering team builds end-to-end data platforms and automated reporting to drive executive-level insights across multi-billion-dollar services, with a focus on data pipelines, event-driven architectures, and revenue attribution.

Responsibilities

• Design and build end-to-end data platforms for new AWS AI services, defining schemas, data models, ETL pipelines, and analytics infrastructure where none exists today
• Build and maintain production ETL/ELT pipelines using AWS Glue, Airflow, Spark, and Python to source data from operational, commercial, and telemetry systems into unified data models
• Develop agentic data workflows, automated reporting pipelines that leverage AI/ML to generate business insights, WBR summaries, and anomaly detection without manual intervention
• Create event-driven data architectures using CDK, Lambda, SNS/SQS, and S3 event notifications to support real-time data ingestion and processing
• Build executive dashboards and self-serve analytics using QuickSight that serve VP/GM-level leadership across multiple service lines
• Own revenue data accuracy, implement and validate revenue attribution models, discount calculations, and financial data pipelines that feed CFO-mandated reporting
• Design data models that support both operational analytics (feature adoption, customer health, churn signals) and financial reporting (revenue, billing, forecasting)
• Collaborate with Product Managers, Finance, Service Engineering, GTM, and Data Science teams to translate business questions into scalable data solutions
• Optimize pipeline performance, reduce runtimes, eliminate redundant processing, and improve SLA compliance across production workloads
• Mentor engineers, contribute to team standards, and drive a culture of automation, code quality, and operational excellence

Requirements

• 5+ years of data engineering experience
• 3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes
• 3+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling
• Experience with data modeling, warehousing and building ETL pipelines

Technologies

• AWS Glue
• Airflow
• Spark
• Python
• CDK
• Lambda
• SNS
• SQS
• S3
• Redshift
• Athena
• QuickSight
• Bedrock
• SageMaker
• EMR
• Kinesis
• FireHose
• IAM

Benefits

• Health insurance (medical, dental, vision, prescription, Basic Life & AD&D, optional Supplemental life, EAP, mental health support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement)
• 401(k) matching
• Paid time off
• Parental leave
• Sign-on payments and RSUs

A Day In The Life

• Design data models for newly launched AWS AI services
• Build and deploy ETL pipelines to onboard telemetry and revenue data
• Validate data accuracy across financial reporting systems
• Architect CDK-based event-driven pipelines
• Collaborate with Product Managers to define launch metrics
• Resolve data discrepancies surfaced by Finance
• Optimize production queries that feed VP-level weekly business reviews

About The Team

The AI Services Data Engineering team builds the data infrastructure behind AWS's Agentic AI portfolio — Amazon Bedrock, AgentCore, QuickSight, Q Business, Kendra, Kiro, and Transform. Our data powers the metrics and reporting that flow up to Amazon's CEO and CFO, supporting S-Team level visibility into Agentic AI revenue, adoption, and growth. We build automated WBR reporting with agent-generated summaries, revenue attribution models for multi-billion dollar pricing programs, and launch telemet