What you'll doIn 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.
Responsibilities- 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
Who you areWe'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
Preferred qualifications- 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
