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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer II, AAE - **Company:** Amazon.com, Inc. - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Salary:** $132,100.0 - $178,800.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Big Data, Software Quality, Databases, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Stores, Graph Database, Identity and Access Management, Python (Programming Language), BIG-IP Global Traffic Manager (GTM), Delivery Pipeline, Apache Spark, Electronic Medical Records, Event Driven Architecture, Build Management, AI Platforms, AWS Glue, Real Time Data, Non-relational Database, Data Management, Amazon Simple Queue Service (SQS), Data Pipelines, Amazon Redshift - **Published:** June 20, 2026 - **Apply:** https://www.juju.com/job/00000000g9qsj7 ## About the Role 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 ## Description 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 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 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 be architecting a CDK-based event-driven pipeline, collaborating with Product Managers to define launch metrics, resolving data discrepancies surfaced by Finance, or optimizing production queries that feed into VP-level weekly business reviews. Your deliverables ship to production on a regular cadence and are consumed directly by senior leadership for strategic decision-making. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk)