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Coinbase

Analytics Engineer Intern

Hybrid - San Francisco, CAinternshipinternAdded 3 days ago

About this role

Coinbase is hiring a 12-week summer analytics engineering intern to help build and maintain data infrastructure that powers decision-making across the organization. You'll develop SQL pipelines, create dimensional data models, and collaborate with stakeholders to deliver production-quality datasets and insights.

What you'll do

  • Build and maintain dimensional data models transforming raw data into clean, reliable datasets
  • Develop production SQL pipelines ensuring data quality, freshness, and documentation standards
  • Partner with cross-functional teams to understand data needs and deliver self-serve analytics
  • Leverage generative AI tools to streamline data development and modeling workflows
  • Investigate data quality issues and implement tests to catch problems before reaching consumers

What they're looking for

  • Advanced SQL (joins, window functions, aggregations)
  • Python or other programming languages
  • Data modeling and dimensional design
  • Generative AI tools
  • Technical communication and documentation
  • Data quality testing and validation
  • ETL/pipeline development
  • Analytics and business intelligence

Benefits

  • Mentorship from senior analytics engineers
  • Hands-on experience with production data systems at scale
  • Exposure to crypto-native fintech operations
  • Quarterly in-person collaboration sessions (surges)
  • Hybrid work arrangement in San Francisco
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Coinbase

Coinbase builds cryptocurrency and financial services infrastructure, with a focus on internal platforms, developer tools, and compliance systems. The company is hiring software engineers, platform engineers, and analytics engineers to enhance developer infrastructure, support operations, power compliance workflows, and deliver data analytics solutions.

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Likely interview questions

  • Walk us through a complex SQL query you've written—what made it challenging and how did you optimize it?
  • Describe your experience building a data pipeline. What testing or monitoring did you implement to ensure quality?
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