Develop production-quality Extract, Transform, and Load (ETL) workflows and data pipelines running in a distributed, containerized, cloud-native environment to support ingestion, transformation, and dissemination of complex social science research data and metadata from a variety of data sources.  Develop tools and software to process archival and statistical file formats including SPSS, SAS, Stata, and R.  Use AWS Cloud products to design and manage relational and non-relational data storage services.  Write SQL queries for creating and developing data reports that cater to the needs of the consumers of ICPSR datasets.  Apply best practices in software development and data management to improve curation workflows in a secure data environment by building a library of reusable scripts and tools orchestrated by an automated jobs framework.  Develop secure, scalable, and resilient REST (representational state transfer) APIs (application programming interfaces) in Python to expose data as a service to upstream producers and downstream consumers.  Implement monitoring solutions for data engineering products and provide operational support to pipelines and APIs in production.  Research and identify open source and COTS (commercial off the shelf) tools to solve challenging problems while minimizing the need for custom code.