Clera
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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.
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.
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).
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