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# Senior Data Engineer, AWS Infrastructure Supply Chain Automation

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $154,600.0 - $209,100.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Amazon S3, Business Analytics Applications, Big Data, Software Documentation, Databases, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, Apache Hadoop, Apache Hive, Python (Programming Language), Node.Js, Standard Sql, Scala (Programming Language), Software Engineering, SQL Databases, Data Logging, Scripting, Apache Spark, Electronic Medical Records, Data Lakes, Data Objects, Amazon Simple Queue Service (SQS), Programming Languages
- **Published:** October 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=dc99671020a2da66

## About the Role

* 5+ years of data engineering experience
 * Experience with data modeling, warehousing and building ETL pipelines
 * Experience with SQL
 * Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
 * Experience mentoring team members on best practices

PREFERRED QUALIFICATIONS

 * Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
 * Experience operating large data warehouses

## Description

* Develop and maintain automated ETL pipelines (with monitoring) using scripting languages such as Python, Spark, SQL and AWS services such as S3, Glue, Lambda, SNS, SQS, KMS.
 * Implement and support reporting and analytics infrastructure for internal business customers.
 * Develop and maintain data security and permissions solutions for enterprise scale data warehouse and data lake implementations including data encryption and database user access controls and logging.
 * Develop data objects for business analytics using data modeling techniques.
 * Develop and optimize data warehouse and data lake tables using best practices for DDL, physical and logical tables, data partitioning, compression, and parallelization.
 * Develop and maintain data warehouse and data lake metadata, data catalog, and user documentation for internal business customers.
 * Work with internal business customers and software development teams to gather and document requirements for data publishing and data consumption via data warehouse, data lake, and analytics solutions.