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Clera

AWS Data Engineer

remote (Remote)$135.2k–$135.2kfulltimemidAdded 2 days ago

About this role

Build and maintain AWS-based data pipelines for a large enterprise, handling data ingestion from legacy systems through to delivery of clean, analytics-ready datasets. You'll design ETL/ELT workflows using AWS Glue, manage S3 storage, implement data quality checks, and automate deployments via GitHub Actions.

What you'll do

  • Design and implement ETL/ELT workflows using AWS Glue to process source data from mainframe and legacy systems
  • Stream and ingest data into AWS S3, managing storage lifecycle, retention policies, and archival strategies
  • Perform data reconciliation, validation, and quality checks to ensure accuracy and reliability
  • Transform and curate data, provisioning clean datasets through AWS Aurora and PostgreSQL RDS
  • Build and maintain data streaming pipelines using Kafka
  • Automate workflows and manage CI/CD deployments using GitHub and GitHub Actions

What they're looking for

  • AWS Glue (ETL/ELT design and implementation)
  • AWS cloud services (S3, RDS, Aurora, Kafka)
  • Data pipeline architecture and lifecycle management
  • Data quality, validation, and reconciliation processes
  • GitHub and GitHub Actions (CI/CD automation)
  • PostgreSQL and relational databases
  • Legacy system and mainframe data integration
  • AI tools for workflow automation

Benefits

  • 100% remote work
  • Hands-on individual contributor role with ownership
  • Opportunity to work with modern AWS data infrastructure
  • Use of AI tools to enhance 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 your experience designing and implementing ETL/ELT pipelines in AWS Glue—what was the most complex workflow you've built?
  • Tell us about a time you implemented data quality checks and reconciliation processes; how did you handle data discrepancies?
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