Data Analyst, Financial Data Engineering

Details

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. Stripe’s mission is to increase the GDP of the internet.

About the Team

Data Science at Stripe is a vibrant community where data analysts and data scientists learn and grow together. The team works with fundamental Stripe data to drive company-wide initiatives. Data Analysts are hired across a variety of analytics roles and teams, in line with the business needs and domain they support.

Responsibilities

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows supporting production-grade financial reporting, risk measurement, and operational decision-making for Treasury Finance.
• Leverage AI tools, including code assistants and LLM-based agents, to accelerate pipeline development, data quality automation, reconciliation, and documentation while maintaining quality.
• Model and transform raw data into clean, well-documented datasets supporting Treasury Finance decision-making, including float positions, cash explainability, risk exposures, and liquidity management.
• Establish and enforce data quality standards through testing, monitoring, and pipeline health alerting.
• Own data freshness service-level agreements, operational alerting, and incident response for data domains supporting risk- and finance-critical workflows.
• Partner with Treasury Finance, data scientists, analysts, and engineers to define data requirements and deliver trusted, reusable financial data products.
• Translate Treasury Finance business requirements into data architecture decisions, anticipate future needs, and help drive data strategy.
• Build self-service tooling and analytics layers that enable stakeholders to access and explore trusted data autonomously.
• Translate complex business requirements into reliable data models.
• Own end-to-end pipeline development, from raw data ingestion to clean, consumption-ready datasets.
• Work with leaders to prioritize high-impact data investments.
• Architect the data layer that enables self-service analytics and deliver actionable business recommendations through rigorous analysis and data storytelling.

Requirements

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, beyond scripting.
• Experience with distributed data frameworks such as Spark for writing and debugging data pipelines.
• Experience with workflow orchestration tools such as 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, including 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

• Experience at a growth-stage internet or software company.
• Experience working with Finance or Treasury teams.
• Understanding of treasury and finance concepts, including float positions, foreign exchange exposure, cash reconciliation, balance sheet usage, and liquidity management.
• Experience with data quality frameworks, data contracts, tiering or classification, or service-level agreement management.
• Experience creating leadership-level reporting, such as quarterly business reviews and monthly business reviews.
• Experience building financial reporting infrastructure, including automated treasury processes, regulatory reporting, or finance close.
• Proficiency with AI tools, including code assistants and LLM agents, for accelerating pipeline development and data quality automation.
• Interest in how data products enable automated or agentic workflows, with an understanding that data quality determines the reliability of downstream decisions.

Employment

Full time.