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# Data Engineer, Workforce Solutions

- **Company:** Amazon.com, Inc.
- **Location:** Bellevue, WA, United States
- **Experience:** Experienced
- **Salary:** $132,100.0 - $178,800.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Airflow, Amazon S3, Big Data, Cloud Computing, Databases, Data Architecture, Information Engineering, Extract Transform Load (ETL), Data Structures, Data Stores, Data Warehousing, Graph Database, Apache Hadoop, Apache Hive, Identity and Access Management, Python (Programming Language), Online Analytical Processing, Node.Js, Role-Based Access Control, Scala (Programming Language), Software Construction, SQL Databases, Data Processing, Scripting, Data Storage Technologies, System Availability, Apache Spark, Electronic Medical Records, Data Lakes, AWS Glue, Non-relational Database, Data Management, Functional Programming, Software Coding, Data Pipelines, Amazon Redshift, Programming Languages
- **Published:** June 3, 2026
- **Apply:** https://www.jobmonkeyjobs.com/career/27732776/Data-Engineer-Workforce-Solutions-Washington-Bellevue-7375

## About the Role

3+ 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

Preferred Qualifications

 - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
 - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
 - Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

## Description

* Design, develop, implement, test, document, and operate large-scale, high-performant data structures for big data analytics.
 * Translate business requirements into robust, scalable, operable solutions that work well within the overall data architecture.
 * Solving complex problems by aggregating multiple large datasets to enable informed decision making.
 * Working with large datasets and developing big data pipelines that move data from source systems to data warehouses, S3 data lakes, and other data storage and processing systems using Big Data and Cloud technologies.
 * Implementing data structures using best practices in data modeling, ELT/ETL (Extract/Transform/Load) processes, using big data tools such as Spark, Hive, SQL, Apache Airflow, AWS Glue, EMR, Lambda, S3, Redshift and OLAP technologies.
 * Following software engineering best practices in writing code (using programming languages like Python, SQL and Scala), reviewing code with peers and testing for all related data processing systems.
 * Maintaining and optimizing infrastructure required to support scalable and high availability data lakes and data pipelines to store and process petabyte scale datasets.


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