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).