Data Engineer II, AAE
About the role
AWS AI Services is one of the largest and fastest-growing business units within AWS, powering services like Amazon Bedrock, AgentCore, QuickSight, Q Business, Kendra, and Kiro. Our Data Engineering team builds the intelligence infrastructure that makes this portfolio measurable — from revenue attribution and launch telemetry to agent-generated business reviews that serve VP-level leadership weekly.
Key job 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
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 telemetry platforms for new GA services. We ship weekly, operate across multiple VP orgs, and value automation over manual work, clean data models over quick fixes, and engineers who own their domain end-to-end.
Basic Qualifications
5+ 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 data modeling experience
Experience with data modeling, warehousing and building ETL pipelines
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 providing technical leadership and mentoring other engineers for best practices on data engineering