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Yes Energy

Data Engineer I

Boston, Massachusetts, United States, Boulder, Colorado, United States$90k–$105kfull-timeentryAdded today

About this role

Yes Energy seeks a Data Engineer I to join their Data Quality team, supporting the development and automation of data quality processes across Oracle and Snowflake systems for electric power market analytics. You'll identify recurring data issues, perform root cause analysis, build monitoring and observability frameworks, and help automate manual workflows to ensure reliable, trusted data for customers making critical grid and trading decisions.

What you'll do

  • Build and maintain data quality frameworks, monitoring queries, alerts, and checks across Oracle and Snowflake systems
  • Identify and automate repetitive data quality tasks using SQL, scripts, and AI-assisted tools
  • Analyze trends in recurring data issues and conduct root cause investigation
  • Partner with Data Collections, Engineering, Support, and Product teams to investigate and resolve data quality problems
  • Document data quality findings, known issues, processes, and provide clear findings to support issue resolution
  • Participate in on-call rotations to support clients with complex data questions

What they're looking for

  • SQL and relational database experience
  • Oracle and/or Snowflake
  • Data quality and data observability frameworks
  • Root cause analysis and troubleshooting
  • Data monitoring and alerting
  • AI or ML-assisted data analysis techniques
  • Technical communication to diverse audiences
  • Problem-solving and analytical thinking

Benefits

  • Hybrid work arrangement (2 days in office)
  • Locations in Boston, MA or Boulder, CO
  • Work with industry-leading electric power data and analytics platform
  • Opportunity to support grid reliability and energy transition
  • Cross-functional collaboration with engineering and product teams
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Yes Energy

Website
yesenergy.com
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Likely interview questions

  • Walk us through a time when you identified a recurring data issue—how did you approach diagnosing the root cause and preventing it from happening again?
  • Describe your experience building reusable data quality checks or monitoring frameworks. How did you make them scalable for future use?
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