Data Engineer II on the AWS AI Services Data Engineering team focuses on delivering end-to-end data platforms and automated reporting that provide executive-level insights across multi-billion-dollar services. The role centers on building robust data pipelines, event-driven architectures, and revenue attribution capabilities to support strategic decision making.
Overview
Location: Seattle, WA (onsite).
Salary: USD 132,100 - 178,800 per year.
Minimum experience requirement: 3 years.
Responsibilities
- Architect and implement comprehensive data platforms for new AWS AI services, establishing schemas, data models, ETL/ELT pipelines, and analytics infrastructure from the ground up.
- Develop and operate production ETL/ELT pipelines using AWS Glue, Airflow, Spark, and Python to ingest data from operational, commercial, and telemetry systems into unified data models.
- Design autonomous data workflows and reporting pipelines that apply AI/ML to generate business insights, weekly business review summaries, and anomaly detection without manual intervention.
- Create event-driven data architectures leveraging CDK, Lambda, SNS/SQS, and S3 event notifications to enable real-time data ingestion and processing.
- Build executive dashboards and self-service analytics with QuickSight for VP and GM level leadership across multiple service lines.
- Ensure revenue data accuracy by implementing and validating revenue attribution models, discount calculations, and financial data pipelines feeding CFO-mandated reporting.
- Design data models supporting 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 by reducing runtimes, eliminating redundant processing, and improving SLA compliance across production workloads.
- Mentor engineers, contribute to team standards, and foster a culture of automation, code quality, and operational excellence.
Requirements
- 5+ years of data engineering experience.
- 3+ years developing and operating large-scale BI data structures for analytics using ETL/ELT processes.
- 3+ years developing and operating large-scale BI data structures with data modeling experience.
- Experience with data modeling, data 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 including medical, dental, vision, prescription, Basic Life & AD&D with optional supplemental life plans, EAP, mental health support, Medical Advice Line, Flexible Spending Accounts, and 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 design data models for newly launched AWS AI services, construct and deploy ETL pipelines to onboard telemetry and revenue data, and validate data accuracy across financial reporting systems. A typical day may involve crafting a CDK-based event-driven pipeline, partnering with Product Managers to define launch metrics, addressing data discrepancies raised by Finance, or tuning 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, including Amazon Bedrock, AgentCore, QuickSight, Q Business, Kendra, Kiro, and Transform. This team powers metrics and reporting that inform senior leadership, providing visibility into Agentic AI revenue, adoption, and growth. The work includes automated weekly business reviews with agent-generated summaries, revenue attribution models for large pricing programs, and launch telemetry.
