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CareerSwift

Data Engineer

United States (Remote)$105k–$140kfulltimemidAdded today

About this role

CerebriOS is hiring a Data Engineer to build and maintain data pipelines for their mid-market analytics platform. You'll design data ingestion and transformation processes, optimize SQL workflows, and collaborate with engineers and analysts to ensure reliable, well-structured data that powers reporting and dashboards.

What you'll do

  • Design, build, and maintain reliable data pipelines and ETL/ELT processes
  • Develop data ingestion and transformation workflows for multiple data sources
  • Build and optimize SQL-based data transformations and queries
  • Monitor pipelines, troubleshoot failures, and resolve data quality issues
  • Collaborate with backend engineers and analysts to define data structures for analytics use cases
  • Document data models, pipelines, and business logic for team clarity

What they're looking for

  • SQL and relational database design
  • Data pipeline development and maintenance
  • Python or similar data engineering language
  • Data modeling and ETL/ELT processes
  • Cloud-based data infrastructure (AWS or GCP)
  • Data quality and troubleshooting
  • Communication and cross-functional collaboration
  • dbt, Airflow, or similar orchestration tools (nice to have)

Benefits

  • Remote-first working environment
  • Professional development opportunities
  • Competitive compensation package
  • Supportive benefits designed for team wellbeing
  • Cross-functional collaboration with product and engineering teams
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CareerSwift

CareerSwift builds an AI-powered job search platform with a focus on reliable infrastructure and polished user experiences. The company is hiring DevOps engineers to manage cloud infrastructure and CI/CD pipelines, frontend developers to build React-based interfaces, and QA engineers to ensure product quality through manual and automated testing.

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Likely interview questions

  • Walk us through a complex data pipeline you designed—what challenges did you face and how did you ensure data quality?
  • Describe your experience with SQL optimization and how you've improved slow-running queries in production.
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