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# Data Engineer, Support Product&Services

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Salary:** $111,400.0 - $185,000.0
- **Contract:** Permanent contract
- **Skills:** Big Data, Data Definition Language, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Query Languages, IBM InfoSphere DataStage, Apache Hadoop, Apache Hive, Python (Programming Language), Korn Shell, Machine Learning, MultiDimensional EXpressions, Scala (Programming Language), PL-SQL, SQL Databases, SQL Server Integration Services, Scripting, Apache Spark, Electronic Medical Records, Agentic-AI, Data Lakes, Machine Learning Operations, Stream Processing, Data Pipelines
- **Published:** October 22, 2026
- **Apply:** https://www.jobmonkeyjobs.com/career/28126642/Data-Engineer-Support-Product-Services-Washington-Seattle-7375

## About the Role

1+ years of data engineering experience

 - Experience with data modeling, warehousing and building ETL pipelines
 - Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
 - Experience with one or more scripting language (e.g., Python, KornShell)

Preferred Qualifications

 - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
 - Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.

## Description

Amazon Advertising's Self-Service Support team ensures that advertisers using our applications can answer their own questions or solve their own problems whenever possible through Help content - and have an easy path to contact Amazon when it's not. We're looking for a curious and resourceful Data Engineer to own our infrastructure, deploy production models, fine-tune our data pipelines, and drive our agentic AI initiatives., In this role, you'll be the engineering backbone of our team - owning the systems that power intelligent, self-service experiences for advertisers at scale. You'll deploy ML models into production, optimize data pipelines for performance and reliability, and lead the development of agentic workflows that autonomously reason and act on behalf of our customers., **Infrastructure Ownership** - Design, build, and maintain scalable data infrastructure (data lakes, warehouses, streaming systems) that supports our analytics and ML workloads

 - **Production Model Deployment** - Own the end-to-end lifecycle of deploying ML models into production, including CI/CD pipelines, model serving, monitoring, and rollback strategies

 - **Pipeline Engineering & Optimization** - Develop and fine-tune ETL/ELT pipelines for performance, reliability, and cost efficiency; instrument for observability and SLA adherence

 - **Agentic AI Development** - Drive the design and implementation of agentic workflows - autonomous systems that reason, plan, and take actions to improve advertiser self-service experiences

 - **Reliability & Scalability** - Ensure production systems meet uptime, latency, and throughput targets; lead incident response and root-cause analysis

 - **Collaboration** - Partner with Data Scientists, Applied Scientists, Product, and Content teams to translate research into production-ready solutions that help advertisers help themselves