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Prolaio

Data Engineer

Chicago, ILFrom $134kmidAdded today

About this role

Prolaio seeks a Data Engineer to design and optimize data pipelines on GCP that support analytics and machine learning in a precision cardiology platform. You'll work with data scientists and product teams to build scalable data lakes while maintaining healthcare compliance and security standards.

What you'll do

  • Develop and operate data lakes on Google Cloud Platform with focus on scalability and performance
  • Build data pipelines using Dagster, dbt, and other tools to transform raw data for analytics
  • Automate workflows and support machine learning models using Python and scripting
  • Ensure data processes comply with security best practices, PII/PHI regulations, and healthcare standards
  • Collaborate with data scientists, product teams, and engineers to align data solutions with business needs
  • Troubleshoot complex technical challenges and drive continuous improvement initiatives

What they're looking for

  • Google Cloud Platform (GCP)
  • Python and SQL
  • Dagster or similar orchestration tools (Airflow, Prefect)
  • BigQuery or comparable cloud data warehouses (Snowflake, Databricks, Redshift)
  • Data lake design and management
  • Healthcare data compliance (PII/PHI)
  • dbt or similar data modeling tools
  • Problem-solving and cross-functional communication

Benefits

  • Competitive salary with performance bonus and equity
  • Medical, dental, and vision insurance with strong company contributions
  • HSA, FSA, and commuter benefits
  • $1,200 annual Lifestyle Spending Account
  • Generous paid time off and sick leave
  • Opportunity to work on impactful healthcare innovation
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Prolaio

Prolaio builds a mobile application that connects patients, care teams, and researchers to support heart care management. The company is hiring Android Developers to enhance the app through feature development, performance optimization, and API integration.

View all jobs at Prolaio

Likely interview questions

  • Walk us through your experience building and managing data lakes on cloud platforms—what challenges have you encountered and how did you address them?
  • Tell us about a complex data pipeline you've built. What orchestration tool did you use, and how did you ensure reliability and performance?
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