AWS Data Engineer

ABOUT THE ROLE This is a fully hands-on, individual contributor Data Engineering role embedded within a large-scale healthcare data platform initiative. You'll be responsible for building and maintaining AWS-based data pipelines that ingest mainframe source data, process and validate it, and deliver clean, consumer-ready datasets — work that directly supports critical downstream analytics and operations. WHAT YOU'LL DO - Stream and process mainframe source data into AWS S3, handling ingestion, reconciliation, and validation workflows. - Design and implement ETL/ELT pipelines using AWS Glue to curate and transform raw data into reliable, queryable datasets. - Provision clean, consumer-ready data through AWS Aurora/RDS PostgreSQL relational databases. - Manage S3 storage including data retention policies, archival strategies, and lifecycle management. - Automate deployments and orchestrate workflows using GitHub Actions and CI/CD pipelines. - Leverage AI tools to improve engineering productivity and automate data workflow processes. - Take ownership of deliverables and drive work to completion with minimal oversight. WHAT WE'RE LOOKING FOR - 5+ years of professional Data Engineering experience delivering data pipelines, ETL/ELT workflows, or data platform solutions. - Hands-on production experience with AWS services including S3, Glue, and RDS/Aurora. - Proven experience building and maintaining real-time data streaming pipelines using Kafka. - Strong background in data reconciliation, data quality checks, and validation processes. - Experience with AWS RDS or Aurora PostgreSQL for data provisioning and querying. - Proficiency with GitHub repository management and GitHub Actions for CI/CD automation. - Experience using AI tools to automate workflows and boost productivity. - Experience with MongoDB or other NoSQL databases is a plus. - Familiarity with mainframe or legacy system data integration is a plus, but not required. COMPENSATION & BENEFITS $65/hr (W2) LOCATION Fully remote (US).