Software Engineer | Data Platform

Software Engineer | Data Platform

This job is no longer open

Ramp is building the next generation of finance tools—from corporate cards and expense management, to bill payments and accounting integrations—designed to save businesses time and money with every click. Over 12,000 customers cut their expenses by 3.5% per year and close their books 8x faster by switching to the Ramp platform.

Founded in 2019, Ramp powers the fastest-growing corporate card and bill payment software in America and enables billions of dollars of purchases each year. Ramp continues to grow quickly, more than doubling its revenue run rate in the first half of 2022.

Valued at $8.1 billion, Ramp's investors include Founders Fund, Stripe, Citi, Goldman Sachs, Coatue Management, D1 Capital Partners, Redpoint Ventures, General Catalyst, and Thrive Capital, as well as over 100 angel investors who were founders or executives of leading companies. The Ramp team comprises talented leaders from leading financial services and fintech companies—Stripe, Affirm, Goldman Sachs, American Express, Mastercard, Visa, Capital One—as well as technology companies such as Meta, Uber, Netflix, Twitter, Dropbox, and Instacart. Ramp was named Fast Company’s #1 Most Innovative Company in North America in 2023 and #5 on LinkedIn Top Startups 2022.

About the Role

The Data Platform team develops and owns the systems that enable Ramp's reporting and strategic decision-making, as well as integrating machine learning models into our Risk systems and the product. As a member of the Data Platform team, you’ll build and maintain the infrastructure that enables Ramp to realize value from data. You’ll also partner with Ramp’s analytics engineers, data scientists, and other data professionals to build internally and externally facing data products.

Our ideal candidate is excited about building systems for data collection, processing, storage, and retrieval, and is also passionate about making these systems observable, reliable, scalable, and highly automated.

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 Data Scientists, Analytics Engineers, Engineers, and Operations teams
  • Build the batch and streaming data pipelines critical to Ramp’s daily operations using Airflow, Snowflake, Materialize, and other data processing technologies
  • Collaborate with stakeholder teams on building and productionizing analytical products and machine learning models
  • Build reliable, scalable, maintainable, and cost-efficient systems across the stack

What You Need

  • Minimum 2 years of experience with workflow orchestrators like Airflow, Dagster, or Prefect
  • Minimum 2 years of 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
  • Strong Python programming skills
  • Track record of building highly-reliable infrastructure for data storage and processing

Nice to Haves

  • Expertise with AWS
  • Expertise with the Modern Data Stack - dbt, Looker, Snowflake, and Fivetran
  • Expertise with building and deploying machine learning systems.
  • Experience with Terraform and Datadog

Compensation

  • The annual salary/OTE range for the target level for this role is $153,000-$180,000 + target equity + benefits (including medical, dental, vision, and 401(k)

Ramp Benefits (for U.S. based employees)

  • 100% medical, dental & vision insurance coverage for you
    • Partially covered for your dependents
    • One Medical annual membership
  • 401k (including employer match)
    • Please note only 401k contributions made while employed by Ramp are eligible for an employer match
  • Unlimited PTO
  • Fertility HRA Up to $5,000 per year
  • WFH stipend to support your home office needs
  • Wellness stipend
  • Parental Leave
  • Relocation support
  • Pet insurance
This job is no longer open
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