Coinbase
Senior
Model the data that powers analytics and experimentation across Coinbase's Platform org
Interview for a Senior Analytics Engineer role on Coinbase's Platform team, owning end-to-end data modeling for assigned business domains — star/snowflake schemas, dbt/Airflow pipelines on Snowflake-class warehouses, and dashboards in Looker/Tableau. Expect deep questions on modular data modeling and ETL/ELT design, advanced SQL and Python, and how you use LLM prompt engineering responsibly inside internal data tooling, fully remote-first with quarterly in-person 'surges.'
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What this interview tests
- End-to-end modular data modeling (star/snowflake schemas) for a business domain
- Production ETL/ELT pipeline design with dbt and Airflow on Snowflake/Databricks-class warehouses
- Advanced SQL and Python (OOP) for reusable data frameworks and UDFs
- Cross-functional partnership with Engineering, Product, and Data Science to close data gaps
- Dashboarding/visualization for non-technical stakeholders (Looker/Tableau)
- Responsible prompt engineering for LLMs in internal data tooling
Common question themes
Walk me through a domain data model you designed end-to-end, from source systems to a star/snowflake schema
Tell me about a production ETL/ELT pipeline you built with dbt or Airflow and how you handled data quality and CI/CD
Describe a reusable framework, UDF, or internal data app you built that other teams relied on
Give an example where you partnered with Product or Data Science to close a data gap that drove a business decision
How have you used prompt engineering for LLMs in internal tooling, and how did you keep human oversight in the loop
How do you decide between modeling purity and shipping speed when a stakeholder needs something urgently
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