Original listing text, shown exactly as published by the company.
About the Role
We’re looking for someone to help lead the future of analytics at Ramp. This person will enable Ramp to get 1% better every day by developing data products and insights. They will partner closely with business stakeholders and product, engineering, and design counterparts to prioritize and execute on work, improve reporting, as well as drive results and process improvements.
What You’ll Do
- Full stack development, building models to consume, transform, and expose data to stakeholders and production systems
- Drive a culture of experimental design, testing agenda, and best practices
- Contribute to the culture of Ramp’s data team by influencing processes, tools, and systems that will allow us to make better decisions in a scalable way
- Collaborate with P/E/D/D (product, engineering, data and design) teams to develop product roadmaps and measure success
- Work closely with data engineering teams to capture, move, store, and transform raw data into highly actionable insights, and partner with business teams to turn those insights into action
What You Need
- Minimum of 4 years of industry experience as a Data Scientist
- Strong knowledge of SQL (preferably Redshift, Snowflake, BigQuery) and how to write efficient SQL queries
- Familiarity with BI tools (preferably Looker, Omni, Sigma, Hex or equivalent) and experience distributing data insights via reports and dashboards
- Track record of shipping high quality products and features at scale
- Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions
Nice-to-Haves
- Experience with the modern data stack (Fivetran / Snowflake / dbt / Looker / Hightouch or equivalents)
- Strong perspective on analytics engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development)
- Experience within the payments and financial technology space
- Familiarity with B2B enterprise sales cycle metrics and processes
