Data Analyst, Financial Data Engineering

Stripe · New York · 7112 Data Science · listed July 22, 2026

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The shape of it

Seniority
Senior
Experience asked
6+ years
Where
Not stated
Requirements listed
9
Length
707 words

In the posting’s own words

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 it asks for · 9

  • 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

Also a plus

  • 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

What the job covers

  • 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

Tools and skills named

Data
  • Data pipelines4×
  • ETL2×
  • Airflow
  • Data modeling
  • Spark
Ways of working
  • Testing3×
  • Technical writing2×
  • Code review
  • Cross-functional
Go to market
  • Pipeline generation4×
Languages
  • Python
  • SQL
Models & research
  • LLM2×
Security & compliance
  • Regulatory

Words the posting leans on

  • data38×
  • experience9×
  • finance8×
  • pipeline8×
  • requirements7×
  • treasury7×
  • business6×
  • analytics5×
  • data quality5×
  • reporting5×
  • data products4×
  • decision4×
  • e.g4×
  • engineering4×
  • pipeline development4×
  • treasury finance4×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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How this page was made

An automated read of a public job posting, fetched August 25, 2026 and last changed by Stripe on August 18, 2026. Every list above is pulled from the posting’s own sentences — nothing rewritten, nothing added, no judgment about the role or the company. Counts and seniority are read off the text by rule, so they can be wrong where the posting is unusual. The original is the only thing that binds. Openings close without warning; check the source before spending an evening on it.