Data Analytics Engineer

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 are looking for a Data Analytics Engineer to build and scale the data models, pipelines, and analytics infrastructure that power decision-making across Parafin. You'll design reliable datasets, transform raw data into trusted business metrics, and partner closely with Product, Engineering, Finance, Risk, and Operations. You'll also leverage modern AI tools to accelerate development and help build scalable, efficient analytics workflows. Your work will enable teams across the company to make better decisions and help us build products that expand access to financial services for small businesses.

Are you a curious, analytical, and collaborative individual with a passion for turning large, complex datasets into actionable insights? If so, come join us!

What You’ll Be Doing:

• Build and maintain scalable data pipelines that power analytics data models and trusted datasets.

• Write efficient SQL to transform, validate, and optimize data for analytics and reporting.

• Leverage AI tools to accelerate development, improve productivity, and streamline analytics workflows.

• Develop and maintain dashboards, reports, and self-service analytics for business stakeholders.

• Partner with Product, Engineering, Finance, Risk, and Operations to understand data needs and deliver data-driven solutions.