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

How candidates describe it

Real Software Engineer interview stories — retold from candidates' public write-ups, with sources.

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