Who we are
About Stripe
Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.
About the team
Data Science at Stripe is a vibrant community where data analysts, data scientists, and engineers learn and grow together. In this role, you'll join the team focused on strengthening Stripe's analytics foundation across the company—setting the technical direction for how analytical data is modeled, stored, governed, and served at scale. This is an infrastructure and platform role, not a business analytics role. You'll operate as a technical leader who shapes the systems, standards, and practices that every analyst and data consumer at Stripe depends on.
What you’ll do
Responsibilities
- Lead architecture reviews and set analytical standards. Own the strategy, technical architecture, and governance model for platforms that make key business metrics consistent, trustworthy, and easy to query at scale. Define and enforce how analytical data is modeled, stored, and served across the company.
- Own the hardest cross-cutting analytical problems. Tackle the most complex, multi-team data challenges—problems that require going arbitrarily deep into unfamiliar domains and coordinating solutions across organizational boundaries.
- Drive org-wide data quality and consistency. Lead critical data quality initiatives that support executive- and board-level decision-making. Ensure that the metrics and data products used to run the business are reliable, well-defined, and widely adopted.
- Shape long-term technical vision. Define a multi-year roadmap for analytics infrastructure, including warehouse design, schema standards, metric integration, real-time analytics systems, and performance optimization—not just executing within these systems, but determining their direction.
- Raise the technical bar across teams. Uplevel the quality of analytical work through mentorship, architecture reviews, standards setting, and building shared tooling and frameworks tha
