Coinbase
Mid
Own the data pipelines that power Coinbase's customer-experience and compliance operations
This is an Analytics Engineer role on Coinbase's GFCO Analytics Engineering team, building the data models and pipelines that give CX Operations and Compliance teams trusted, self-serve metrics. Expect deep SQL/dbt/Airflow questions plus scenarios about turning messy stakeholder asks into governed data products.
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What this interview tests
- Data pipeline design and ownership (dbt, Airflow, cloud warehouses)
- Dimensional data modeling for analytics
- Data quality, testing, and governance in a compliance context
- Translating CX/Compliance stakeholder needs into self-serve analytics
- BI/dashboard tooling (Looker, Tableau)
- Responsible use of generative AI in engineering workflows
Common question themes
Walk me through a data pipeline you designed end-to-end, including modeling choices
How do you build trust in a metric that compliance or leadership relies on?
Describe a time a non-technical stakeholder's ask was vague — how did you scope it into a data solution?
How do you approach testing and monitoring for data pipelines you own?
What's your experience with dimensional modeling and when do you choose star vs. snowflake schema?
How do you decide what to automate vs. keep as a manual/reactive process?
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