Staff Data Analyst

Stripe

About The Position

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. Data Science at Stripe is a vibrant community where data analysts, data scientists, and engineers learn and grow together. In this role, you will 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 will operate as a technical leader who shapes the systems, standards, and practices that every analyst and data consumer at Stripe depends on.

Requirements

10+ years of experience in Data Analysis, Analytics Engineering, Business Intelligence Engineering, or Data Science roles — building data infrastructure, defining metrics, and solving complex analytical problems at scale
Deep expertise in SQL, including demonstrated ability to optimize complex queries, debug data pipelines, and work across large, distributed datasets
Familiarity with AI-assisted development tools (e.g., Claude Code or equivalent) to accelerate data work, automate repetitive tasks, and improve analytical productivity
Proven track record of setting long-term technical vision for analytics platforms — not just executing within them, but shaping their architecture, governance, and adoption strategy
Expertise in warehouse design and metrics infrastructure: schema design, metric layer integration, performance optimization, and building data products that are consistent and trustworthy at scale
Experience leading cross-team or org-wide data initiatives that materially improved data quality, consistency, or accessibility for a broad set of stakeholders
Demonstrated ability to raise the technical bar for teams around you — through mentorship, architecture reviews, standards setting, or building shared tooling that others adopt
Strong communication and influence skills: ability to align senior leaders and cross-functional partners on technical direction, and to communicate complex data problems and recommendations clearly
Comfortable operating across organizational boundaries: proven ability to identify data analytics problems that span multiple teams and drive solutions with technical rigor

Nice To Haves

Master's degree in Mathematics, Statistics, Economics, Engineering, Computer Science, or a related technical field (or equivalent experience, 10+ years of demonstrated impact is weighted equally)
Experience building or governing a semantic layer or metrics layer (e.g., dbt metrics, Looker LookML, or a custom metrics platform)
Experience with distributed data frameworks (Hadoop, Spark) for large-scale data pipeline development and optimization
Prior experience at a high-growth internet or software company navigating rapid scale

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 that other analysts adopt and depend on.
Translate business needs into technical solutions.
Partner deeply with leaders across Stripe to identify the highest-priority data problems, then deliver scalable, well-governed solutions with clear adoption plans and measurable impact.