Data Analyst
- Your values-driven approach aligns well with Stripe’s mission of increasing global economic access.
- You can bring creativity to data storytelling and helping teams see the human impact of insights.
- The role's emphasis on collaboration and enabling others aligns with your desire to help people grow.
- The highly technical, detail-oriented tasks like building and debugging pipelines may feel overwhelming or tedious.
- You might find the structured, results-driven culture challenging for your more idealistic and flexible work style.
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 and data scientists learn and grow together. You'll work with some of the most fundamental data at Stripe, and use that data to help drive company-wide initiatives. We have a variety of Data Analytics roles and teams across Stripe and will seek to align you to the most relevant team based on your background.
What you'll do
In this role, you'll partner deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. You'll work closely with partners to extract insights from the rich and complex data at Stripe. You'll also work with leaders to translate business needs into data problems. You'll build metrics, scalable data pipelines, dashboards, and reports to inform and run the business. You'll deliver actionable business recommendations through analyses and data storytelling.
Who you are
We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
Minimum requirements
- MS/MA + 2 years or BS/BA + 3 years of full-time experience exclusive of internships in Business Intelligence Engineering, Data Analyst, and Business Analyst roles
- Proficiency in SQL
- Proven ability to manage and deliver on multiple projects with great attention to detail
- Ability to clearly communicate results and drive impact
- Ability to design, implement, and maintain data pipelines and dashboards to generate actionable insights based on stakeholder requirements
- Experience collaborating with cross-functional teams to deliver strategic insights, benchmarks, and analyses that provide recommendations
- Ability to enable stakeholders and partners by building self-service tooling and providing training to empower stakeholder teams to be data literate and self-sufficient in autonomous reporting capabilities
Preferred qualifications
- Prior experience at a growth-stage internet or software company
- Experience with distributed data frameworks like Spark to write and debug data pipelines
- Good understanding of development processes and best practices like engineering standards, code reviews, and testing
- Strong statistical knowledge
- Working knowledge of Python
- Experience creating leadership-level reporting, such as QBRs, MBRs
- Proficiency with AI tools to accelerate model development, analysis, and coding
Best personality fits for this role
Your strategic mindset fits well with translating business needs into data problems and building scalable data products.
Read the INTJ career guide →Your strong organizational skills and focus on efficiency align well with building scalable data pipelines and systems.
Read the ESTJ career guide →Your meticulous attention to detail ensures data pipelines and dashboards are accurate and reliable.
Read the ISTJ career guide →