Data Engineer II, AAE

Responsibilities

• Design and implement end-to-end data platforms for new AWS AI services, establishing schemas, data models, ETL/ELT pipelines, and analytics infrastructure from the ground up.
• Create and operate production ETL/ELT pipelines using AWS Glue, Airflow, Spark, and Python to ingest data from operational, commercial, and telemetry sources into unified data models.
• Develop agentic data workflows with automated reporting pipelines that apply AI/ML to generate business insights, weekly business review summaries, and anomaly detection without manual steps.
• Build event-driven data architectures using CDK, Lambda, SNS/SQS, and S3 event notifications to enable real-time data ingestion and processing.
• Develop executive dashboards and self-serve analytics in QuickSight for VP/GM level leaders across multiple service lines.
• Ensure revenue data accuracy by implementing and validating revenue attribution models, discount calculations, and financial pipelines feeding CFO-required reporting.
• Design data models that support both operational analytics (feature adoption, customer health, churn signals) and financial reporting (revenue, billing, forecasting).
• Partner with Product Managers, Finance, Service Engineering, GTM, and Data Science to translate business questions into scalable data solutions.
• Optimize pipeline performance by reducing runtimes, eliminating redundant processing, and improving SLA adherence across production workloads.
• Mentor engineers, contribute to team standards, and foster automation, code quality, and operational excellence.

Requirements

• 5+ years of data engineering experience.
• 3+ years designing and operating large-scale BI data architectures using ETL/ELT processes.
• 3+ years of data modeling experience and managing large-scale BI data structures for analytics.
• Hands-on experience with data modeling, data warehousing, and building ETL pipelines.

Technologies

• AWS Glue
• Redshift
• Athena
• QuickSight
• Bedrock
• SageMaker
• CDK
• Lambda
• SNS
• SQS
• S3
• Airflow
• Spark
• Python
• Kendra
• Kiro

Benefits

• Health insurance (medical, dental, vision, prescription) with Basic Life & AD&D, option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, FSA, Adoption and Surrogacy reimbursement.
• 401(k) matching.
• Paid time off and parental leave.
• Sign-on payments and restricted stock units (RSUs).

A Day In The Life

As a Data Engineer on this team, you will design data models for newly launched AWS AI services, build and deploy ETL pipelines to onboard telemetry and revenue data, and validate data accuracy across financial reporting systems. You may architect a CDK based event-driven pipeline, collaborate with Product Managers to define launch metrics, resolve data discrepancies surfaced by Finance, or optimize production queries feeding VP level weekly business reviews.

About The Team

The AI Services Data Engineering team builds the data infrastructure behind AWS's Agentic AI portfolio — including Bedrock, AgentCore, QuickSight, Q Business, Kendra, Kiro, and Transform. Our data supports metrics and reporting used by executive leadership, enabling visibility into Agentic AI revenue, adoption, and growth. We develop automated weekly business review reporting with agent-generated summaries, revenue attribution models for multi-billion dollar pricing programs, and launch telemetry.