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Remote · Unlimited leave

Finance Data Lead

Parafin · San Francisco · United States

Remote Full time Posted Open until 18 Oct 2026

The job, in the employer's words

About Us:

At Parafin, we’re on a mission to grow small businesses.

Small businesses are the backbone of our economy, but traditional banks often don’t have their backs. We build tech that makes it simple for small businesses to access the financial tools they need through the platforms they already sell on.

We partner with companies like DoorDash, Amazon, Worldpay, and Mindbody to offer fast and flexible funding, spend management, and savings tools to their small business users via a simple integration. Parafin takes on all the complexity of capital markets, underwriting, servicing, compliance, and customer service for our partners.

We’re a tight-knit team of innovators hailing from Stripe, Square, Plaid, Coinbase, Robinhood, CERN, and more — all united by a passion for building tools that help small businesses succeed. Parafin is backed by prominent venture capitalists including GIC, Notable Capital, Redpoint Ventures, Ribbit Capital, and Thrive Capital. Parafin is a Series C company, and we have raised more than $194M in equity and $340M in debt facilities.

Join us in creating a future where every small business has the financial tools they need.

About the Position

We're looking for a Finance Data Lead to sit at the intersection of finance and data engineering on our Finance team. This role requires someone who thinks in queries, models data pipelines, and moves quickly with modern tooling, while also being comfortable discussing a P&L or loan portfolio with senior leadership.

You'll be the go-to person for turning raw financial data into clean, trusted, and actionable outputs — owning everything from ad hoc analyses to scalable reporting infrastructure.

What You’ll Be Doing:

  • Own financial data workflows end-to-end — from sourcing and transforming raw data (SQL, Python, etc.) to building clean, auditable outputs in Excel or BI tools

  • Build and maintain financial models and dashboards that track key metrics such as origination volumes, revenue run rates, and portfolio performance

  • Respond to ad hoc data requests from Finance, Product, and GTM — quickly, accurately, and with clear documentation

  • Leverage AI tools (e.g., Claude, ChatGPT, Copilot) to accelerate analysis, automate repetitive tasks, and surface insights faster

  • Partner with Engineering and Data teams to ensure financial data is correctly structured, accessible, and well-governed

  • Maintain data integrity standards — building in checks, reconciliation logic, and clear audit trails across all financial outputs

  • Support external reporting needs, including materials for investors, lenders, and board-level stakeholders

What We’re Searching For:

Must-Haves

  • 4–7 years of experience in a data-heavy role (data analyst, analytics engineer, financial analyst with strong technical depth, etc.)

  • Expert-level Excel/Google Sheets skills (complex models, dynamic arrays, pivot tables)

  • Strong SQL skills — you can write and optimize queries against large datasets independently

  • Hands-on experience with modern AI tools and a genuine curiosity about how to apply them to financial workflows

  • Baseline fluency in corporate finance concepts such as EBITDA, working capital, and structured finance

  • High degree of trust and discretion — you'll have access to sensitive financial data and are expected to handle it responsibly

  • Strong verbal communication skills — you can translate complex datasets into clear, concise narratives

Nice-to-Haves

  • Experience in fintech, lending, or structured finance (familiarity with loan tapes, origination data, or SPV structures is a plus)

  • Proficiency in Python or R for data manipulation and automation

  • Experience with a BI tool (Looker, Tableau, Mode, etc.)

  • Exposure to DBT, Airflow, or similar data pipeline tooling

Why This Role

  • High-impact, high-visibility work — your outputs are presented in executive and investor materials

  • A seat at the table for data infrastructure decisions within the Finance team

  • Opportunity to define processes and tooling in a fast-moving environment

  • Collaborative team that values rigor, speed, and intellectual curiosity

What We Offer

  • Salary Range: $210k–$250k

  • Equity grant

  • Medical, dental, and vision insurance

  • Unlimited PTO

  • Work-from-home flexibility

  • Commuter benefits

  • Free lunches

  • Paid parental leave

  • 401(k)

  • Employee assistance program

If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please contact us.

Benefits mentioned

  • Salary Range: $210k–$250k
  • Equity grant
  • Medical, dental, and vision insurance
  • Unlimited PTO
  • Work-from-home flexibility
  • Commuter benefits
  • Free lunches
  • Paid parental leave
  • 401(k)
  • Employee assistance program
  • If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please contact us.

Requirements mentioned

  • 4–7 years of experience in a data-heavy role (data analyst, analytics engineer, financial analyst with strong technical depth, etc.)
  • Expert-level Excel/Google Sheets skills (complex models, dynamic arrays, pivot tables)
  • Strong SQL skills — you can write and optimize queries against large datasets independently
  • Hands-on experience with modern AI tools and a genuine curiosity about how to apply them to financial workflows
  • Baseline fluency in corporate finance concepts such as EBITDA, working capital, and structured finance
  • High degree of trust and discretion — you'll have access to sensitive financial data and are expected to handle it responsibly
  • Strong verbal communication skills — you can translate complex datasets into clear, concise narratives
  • Experience in fintech, lending, or structured finance (familiarity with loan tapes, origination data, or SPV structures is a plus)
  • Proficiency in Python or R for data manipulation and automation
  • Experience with a BI tool (Looker, Tableau, Mode, etc.)
  • Exposure to DBT, Airflow, or similar data pipeline tooling

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