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# Data Engineer, PXT Central Science

- **Company:** Amazon.com, Inc.
- **Location:** Arlington, VA, United States
- **Experience:** Experienced
- **Salary:** $152,000.0 - $205,600.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Amazon Web Services, Amazon S3, Data Analysis, Big Data, Code Review, Databases, Data Architecture, Information Engineering, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Stores, Data Systems, Graph Database, Apache Hadoop, Apache Hive, Identity and Access Management, Python (Programming Language), Machine Learning, Node.Js, Software Architecture, Scala (Programming Language), Software Engineering, Software Technical Review, Scripting, Apache Spark, Electronic Medical Records, AWS Glue, Non-relational Database, Feature Extraction, Api Design, Software Coding, Software Version Control, Data Pipelines, Amazon Redshift, Programming Languages
- **Published:** August 31, 2026
- **Apply:** https://dejobs.org/x/x/E76CF7A3CF424BE78386F4F601651351/job/

## About the Role

* 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
 * 3+ years of data engineering experience
 * Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
 * Experience with data modeling, warehousing and building ETL pipelines
 * 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)

Preferred Qualifications

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

## Description

PXTCS is looking for a data engineer with expertise in complex data environments. You will be responsible for enhancing our existing data architecture to further standardize metrics and definitions, building and testing new features, developing end-to-end data engineering solutions for complex analytical problems, and collaborating with economists, data scientists, and software engineers to translate data into actionable insights. Specific responsibilities include:

 * Data Pipeline Development: Design and maintain scalable data pipelines using native AWS services (Glue, EMR, Lambda); build monitoring and error handling for data workflows; optimize performance, reliability, and cost efficiency

 * Model Productionization & API Development: Develop and maintain APIs and data serving layers that productionize science models for downstream consumption; build batch and real-time inference pipelines

 * Data Integration & Quality: Build scalable feature extraction and processing frameworks for diverse data types; develop robust data quality and validation checks; create flexible schemas supporting evolving requirements

 * Cross-team Collaboration: Partner with economics, data science, and software engineering teams to translate analytical requirements into production-ready solutions; participate in technical design reviews and architecture discussions

 * Analytics & Infrastructure: Maintain layered data systems used by economists and scientists; build automated reporting solutions; work across multiple interconnected AWS accounts with security best practices

About the team

PXTCS combines economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements that improve both Amazon's operations and the experience of every Amazonian. Its engineering teams take science-driven insights and models - spanning areas like benefits, compensation, recruiting, voice of employee, management practices, and organizational culture - and turn them into production systems operating at Amazon's scale. PXTCS is an interdisciplinary group where engineering, applied science, and product work side-by-side, and where this team's output directly shapes how Amazon supports its workforce.


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