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).