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 Data Analysts are hired in line with the business needs and domain of the organization they will support.
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 design, build, and own the scalable data infrastructure that powers analytics and reporting across the company.
Day to day, you'll translate complex business requirements into reliable data models, own end-to-end pipeline development from raw data ingestion to clean, consumption-ready datasets, and work with leaders to prioritize the highest-impact data investments. You'll go beyond building dashboards—you'll architect the data layer that makes self-service analytics possible and deliver actionable business recommendations through rigorous analysis and data storytelling.
Design, build, and maintain scalable data pipelines and ETL/ELT workflows that power production-grade financial reporting, risk measurement, and operational decisioning for Treasury Finance
Leverage AI tools (code assistants, LLM-based agents) to accelerate pipeline development, data quality automation, reconciliation, and documentation - expanding technical scope while maintaining quality.
Model and transform raw data into clean, well-documented datasets that serve as the core foundations for decision making for Treasury Finance (e.g. float positions, cash explainability, risk exposures, liquidity management)
Establish and enforce data quality standards through testing, monitoring, and alerting on pipeline health
Establish and own data freshness SLAs, operational alerting, and incident response for your data domains - ensuring production reliability for risk and finance critical workflows
Partner deeply with Treasury Finance, data scientists/analysts, and engineers to define data requirements and deliver trusted, reusable financial data products
Partner deeply with Treasury Finance stakeholders to translate business requirements into data architecture decisions, anticipating needs and helping to drive data strategy rather than reacting to requests
Build self-service tooling and analytics layer that empower stakeholders to access and explore trusted data autonomously
Requirements
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
6+ years of full-time experience in Data Engineering, Analytics Engineering, Business Intelligence Engineering, or a related analytical role
Proficiency in SQL, including complex query optimization and data modeling
Proficiency in Python for data pipeline development, not just scripting
Experience with distributed data frameworks like Spark to write and debug data pipelines
Experience with workflow orchestration tools (e.g. Airflow, Flyte, or equivalent)
Proven ability to design, implement, and maintain production-grade data pipelines and dashboards
Good understanding of development processes and best practices like engineering standards, code reviews, and testing
Ability to clearly communicate results and drive impact with cross-functional partners
Experience owning production data products with defined quality standards, testing, and documentation
Nice to have
Prior experience at a growth-stage internet or software company
Prior experience working with Finance or Treasury teams
Understanding of treasury and finance concepts (e.g., float positions, FX exposure, cash reconciliation, balance sheet usage, liquidity management)
Experience with data quality frameworks, data contracts, tiering/classification, or SLA management
Experience creating leadership-level reporting, such as QBRs and MBRs
Experience building financial reporting infrastructure - e.g. automated treasury processes, regulatory reporting, or finance close
Proficiency with AI tools (code assistants, LLM agents) to accelerate pipeline development and data quality automation
Interest in how data products enable automated/agentic workflows — understanding that data quality determines the reliability of every downstream decision