AWS Data Engineer

Role Summary

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.

Responsibilities

• 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.

Requirements

• 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.
• $65/hr (W2)
• Fully remote (US).