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Arbor

Data Engineer

Remote, near DC (Remote)fulltimemidAdded 1 month ago

About this role

Arbor is seeking a Data Engineer to build and maintain the data infrastructure powering their AI-driven electricity marketplace. You'll own the full pipeline from ingestion through Snowflake transformations, deliver analytics that inform pricing and marketplace decisions, and help integrate AI into data workflows.

What you'll do

  • Design and maintain data pipelines ingesting from GCP production systems and Fivetran into Snowflake
  • Own the dbt transformation layer for energy market data, customer events, and utility rate feeds
  • Build dashboards and analytics in Hex to surface insights for business stakeholders
  • Define data contracts and schema standards to ensure data reliability at scale
  • Partner with engineering and leadership to deliver analytics on pricing, customer behavior, and marketplace performance
  • Explore AI-assisted approaches to data quality monitoring and development automation

What they're looking for

  • dbt modeling and data transformation
  • SQL and Snowflake (schema design, query optimization)
  • Data pipeline development (Fivetran, custom pipelines)
  • Dashboard tools (Hex, Looker, or similar)
  • Python for data engineering or quality tooling
  • GCP data services (BigQuery, Cloud Storage, Pub/Sub)
  • Data testing and documentation practices
  • AI tools for development acceleration

Benefits

  • Competitive salary
  • Meaningful equity
  • Health and other benefits
  • Remote-friendly with flexibility (near DC preferred)
  • High ownership on a lean, fast-moving team
  • Opportunity to shape AI integration in data workflows
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Arbor

Arbor operates an AI-driven electricity marketplace, building data infrastructure and analytics platforms to power pricing and marketplace operations. The company is hiring Data Engineers to design and maintain data pipelines, develop analytics, and integrate AI into their data workflows.

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Likely interview questions

  • Walk us through a dbt project where you designed models for complex, real-time data. How did you think about testing, documentation, and downstream consumer needs?
  • Describe your experience optimizing Snowflake for both query performance and cost. How do you approach schema design trade-offs?
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