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Clera

AWS Data Engineer

remote (Remote)$135.2k–$135.2kfulltimemidAdded today

About this role

Join a healthcare data engineering team to design and maintain end-to-end AWS data pipelines that ingest mainframe data, transform it through ETL/ELT workflows, and provision clean datasets for analytics. This fully remote, hands-on role requires owning data movement from source to consumption at scale, with emphasis on AWS services, data quality, and CI/CD automation.

What you'll do

  • Stream and ingest mainframe source data into AWS S3 using modern pipeline tooling
  • Design and implement ETL/ELT workflows in AWS Glue for data transformation and curation
  • Execute data reconciliation, validation, and quality checks on ingested datasets
  • Provision and manage clean datasets through AWS Aurora and RDS PostgreSQL
  • Manage S3 storage lifecycle, retention policies, and archival strategies
  • Automate and deploy workflows using GitHub Actions and CI/CD pipelines

What they're looking for

  • AWS Glue (ETL/ELT design and implementation)
  • AWS S3, RDS, and Aurora in production
  • Data streaming with Kafka
  • Data reconciliation and quality validation
  • GitHub and GitHub Actions CI/CD
  • AI tools for workflow automation
  • Mainframe data integration
  • Python or SQL for data engineering

Benefits

  • 100% remote, US-based
  • W2 contract engagement
  • Work in healthcare industry
  • Access to modern AWS data stack
  • Opportunity to use AI tools for productivity
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Walk us through a complex ETL pipeline you've built in AWS Glue—how did you handle data quality and reconciliation?
  • Describe your experience integrating mainframe or legacy system data sources. What were the main challenges?
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