Data Engineer II within the AWS AI Services Data Engineering team in Seattle focuses on building end-to-end data platforms and automated reporting to empower executive decision-making across multi-billion-dollar services, with emphasis 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
- 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 and AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
- 401(k) matching
- Paid time off
- Parental leave
- Sign-on payments and 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. On any given day, 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 that feed into 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 telemetry.
