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# Data Engineer, Amazon Prime

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Experienced
- **Salary:** $132,100.0 - $178,800.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Amazon S3, Systems Engineering, Big Data, Code Review, Databases, Information Engineering, Extract Transform Load (ETL), Data Mining, Data Stores, Data Systems, Distributed Systems, Amazon DynamoDB, Graph Database, Identity and Access Management, Python (Programming Language), Machine Learning, Node.Js, Software Architecture, Scala (Programming Language), Software Engineering, Scripting, Apache Spark, Electronic Medical Records, AWS Lambda, AWS Glue, Real Time Data, Non-relational Database, Software Coding, Software Version Control, Serverless Computing, Amazon Elastic Mapreduce (EMR), Amazon Redshift, Programming Languages
- **Published:** October 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ca314be87fed6b18

## About the Role

* 3+ years of data engineering experience
 * Experience with data modeling, warehousing and building ETL pipelines
 * Experience as a data engineer or related specialty (e.g., software engineer, business intelligence engineer, data scientist) with a track record of manipulating, processing, and extracting value from large datasets
 * Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
 * Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS, * 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 building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets

## Description

Build and maintain big data pipelines for machine learning applications at Amazon scale, ensuring stability and performance across terabyte-sized datasets
Advance data engineering infrastructure using tools such as AWS Glue, Spark, Amazon EMR, and EMR Serverless to support science and analytics workloads
Design and implement proactive data quality monitoring and alerting (e.g., anomaly detection) across production pipelines to safeguard downstream models
Collaborate with scientists and analysts to create coherent data models and analysis-ready datasets that improve the customer experience for over 300 million members worldwide
Evaluate and integrate generative AI tools where appropriate to automate data workflows and improve team productivity

A day in the life
You will collaborate closely with Prime business leaders, scientists (economists, research scientists, applied scientists), and engineering leaders to build data solutions. You will work with AWS technologies such as Amazon EMR, Amazon S3, AWS Glue, AWS Lambda, and Amazon DynamoDB to process terabyte-scale data and support real-time access and orchestration across multiple systems. Your work will directly enhance scientific models and data applications that shape the Prime member experience globally. You will also participate in code reviews, design discussions, and team planning alongside both technical and business stakeholders.

About the team
The Prime Member Experience engineering team sits at the intersection of large-scale engineering systems and applied science ,econometrics, machine learning, and AI delivering applications that reach over 300 million customers. Our team includes data engineers, software engineers, and scientists who partner closely to build and operate real-time science products. We are focused on enabling Prime Science adoption worldwide across Amazon, and the growing complexity of our real-time data products creates data engineering challenges that give you hands-on experience with problems the broader industry will face in the years ahead.