AWS Data Engineer

This is a fully hands-on individual contributor role on a data engineering team within the healthcare industry. You will own the end-to-end movement and transformation of data, from ingesting mainframe source streams into AWS through to provisioning clean, consumer-ready datasets. Your work directly enables downstream analytics and operational systems at scale.

Stream and process mainframe source data into AWS S3 using modern data pipeline tooling.

Design and implement ETL/ELT workflows using AWS Glue to curate and transform data.

Perform data reconciliation, validation, and quality checks across ingested datasets.

Provision clean, consumer-ready datasets through AWS Aurora and RDS PostgreSQL databases.

Manage S3 storage including data retention policies, archival strategies, and lifecycle management.

Automate and deploy workflows using GitHub Actions and CI/CD pipelines.

Leverage AI tools to improve engineering productivity and automate data workflows.

5 or more years of professional Data Engineering experience delivering data pipelines, ETL/ELT workflows, or data platform solutions.

Hands-on production experience with AWS Glue for ETL/ELT design and implementation.

Strong working knowledge of AWS S3, RDS, and Aurora in production environments.

Demonstrated experience building and maintaining data streaming pipelines using Kafka.

Experience implementing data reconciliation, data quality checks, and validation processes.

Proficiency with GitHub repository management and GitHub Actions for CI/CD automation.

Experience using AI tools to automate workflows and boost engineering productivity.

Ability to own deliverables and drive work to completion with minimal oversight.

Experience with mainframe data sources or legacy system integration is a plus.

Familiarity with MongoDB or other NoSQL databases is a plus.