Posted:
5/13/2024, 1:48:28 PM
Location(s):
New York, United States
Experience Level(s):
Senior
Field(s):
AI & Machine Learning ⋅ DevOps & Infrastructure ⋅ Software Engineering
Workplace Type:
Remote
Pay:
$166/hr or $345,280 total comp
Ramp is the ultimate platform for modern finance teams. Combining corporate cards with expense management, bill payments, vendor management, accounting automation and more, Ramp's all-in-one solution is designed to save businesses time and money, and free finance teams to do the best work of their lives. Our mission is to help build healthier businesses, and it’s working: over 25,000 businesses on Ramp to save an average 5% and close their books 8x faster.
Founded in 2019, Ramp powers the fastest-growing corporate card and bill payment platform in America, and enables tens of billions of dollars in purchases each year.
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. In 2023, Ramp was named Fast Company’s #1 Most Innovative Company in North America, LinkedIn’s #1 Top Startup in the U.S., a CNBC Disruptor, and a TIME100 Most Influential Company.
About the Role
The Data Platform team develops and owns the systems that enable Ramp's reporting and strategic decision-making and integrate machine learning models into our operational systems and the product itself. 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, applied scientists, software engineers, and other data professionals to build internally and externally-facing data infrastructure & 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, ClickHouse, Kafka, and other data processing technologies.
Collaborate with stakeholder teams on building and productionizing analytical products and machine learning systems.
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.
Strong Python programming skills.
Track record of building highly reliable infrastructure for data storage and processing.
Nice-to-Haves
Expertise with AWS
Previous experience building online machine learning systems.
Previous experience building a feature store.
Experience with Terraform and Datadog
Experience building streaming systems.
100% medical, dental & vision insurance coverage for you
Partially covered for your dependents
One Medical annual membership
401k (including employer match on contributions made while employed by Ramp)
Flexible PTO
Fertility HRA (up to $5,000 per year)
WFH stipend to support your home office needs
Wellness stipend
Parental Leave
Relocation support for NY
Pet insurance
Website: https://ramp.com/
Headquarter Location: New York, New York, United States
Employee Count: 501-1000
Year Founded: 2019
Last Funding Type: Series D
Industries: Finance ⋅ Financial Services ⋅ FinTech