The Data Analyst in Financial Data Engineering at Stripe will partner with various teams to ensure that users, products, and the business have the necessary data models and insights for decision-making. This role involves designing and maintaining scalable data infrastructure that supports analytics and reporting. The Data Science team at Stripe fosters a collaborative environment for analysts and scientists to grow together while addressing critical business needs. The focus is on building reliable data pipelines and delivering actionable business recommendations through data storytelling.

• 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 such as Airflow or Flyte.
• 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.

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows for financial reporting and risk measurement.
• Leverage AI tools to accelerate pipeline development and ensure data quality.
• Model and transform raw data into clean datasets for decision-making in Treasury Finance.
• Establish and enforce data quality standards through testing and monitoring.
• Own data freshness SLAs and ensure production reliability for critical workflows.
• Partner with Treasury Finance and data scientists to define data requirements and deliver trusted financial data products.
• Translate business requirements into data architecture decisions and drive data strategy.
• Build self-service tooling and analytics layers for stakeholders to access trusted data autonomously.