About Ramp

Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.

The problems are high-stakes, data-dense, and unforgiving.

We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.

The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.

If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.

About the Role

The Data Platform builds infrastructure and tools that enable Ramp to realize business value from data. We partner closely with stakeholder teams to build this infrastructure and the applications on top of it. This role is particularly focused on building platforms that support the data science development lifecycle. You’ll partner with applied scientists, AI engineers, Risk engineers, and other ML developers on building infrastructure and tools that enable and accelerate the development of machine learning models. 

What You’ll Do

• Build and integrate the components of Ramp's Analytics Platform and Machine Learning Platform.

• Build tools that improve the agility and data experience of Ramp's Applied Scientists, AI Engineers, and Risk Engineers.

• Collaborate with stakeholder teams on building and productionizing machine learning applications.

• Build reliable, scalable, maintainable, and cost-efficient systems across the stack.

What You Need

• Experience with workflow orchestrators like Airflow, Dagster, or Prefect.

• Experience building infrastructure on AWS, GCP, or Azure.

• Knowledge of SQL and experience with Snowflake, Redshift, BigQuery, or similar databases.

• Intuition around analytics and machine learning, and