DataEngineerII,AAE

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. We are looking for an experienced, self-driven Data Engineer to join a team that operates at the intersection of data engineering and agentic AI. In this role, you won't just build pipelines — you'll design data platforms that power AI agents, build automated reporting systems that replace manual processes, and create the data foundations that prove business impact across a multi-billion dollar service portfolio. You'll work with modern AWS-native data stacks (Glue, Redshift, Athena, QuickSight, Bedrock, SageMaker), build event-driven architectures with CDK, and contribute to agentic workflows that generate executive-level insights autonomously. You should be comfortable operating in ambiguity, designing data models from scratch for new services, and making architectural trade-off decisions that scale. This is a high-visibility role. Your work will directly inform decisions made by VPs, GMs, and the CFO's office — from revenue unification mandates to enterprise deal velocity to AI adoption measurement. 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