Ramp
Mid
Build Ramp's Analytics and Machine Learning Platform to accelerate applied scientists, AI engineers, and risk engineers
This role is on Ramp's Data Platform team, building infrastructure that supports the data science development lifecycle — think workflow orchestration, feature stores, and productionizing ML models for a finance-automation company processing over $200B in annualized spend. You'll need hands-on experience with orchestrators like Airflow/Dagster/Prefect, cloud infra on AWS/GCP/Azure, and warehouses like Snowflake/Redshift/BigQuery, plus strong Python. Ramp explicitly hires for high agency and ownership rather than pedigree — expect questions that probe how you drive a project end to end.
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What this interview tests
- Workflow orchestration (Airflow, Dagster, Prefect)
- Cloud infrastructure (AWS/GCP/Azure) for data/ML platforms
- SQL and data warehousing (Snowflake, Redshift, BigQuery)
- Productionizing ML models and empathy for data science workflows
- Ownership and agency in ambiguous, high-stakes problems
Common question themes
Walk me through a data pipeline you built with Airflow/Dagster/Prefect and what made it reliable
How would you design a feature store or online ML serving system
Tell me about a time you owned an ambiguous infra problem end to end
How do you balance reliability, scalability, and cost efficiency in a data platform
What's your experience partnering with applied scientists or ML engineers on tooling
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