Senior Software Engineer, Data Platform

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 seasoned software engineer to join Parafin’s Infrastructure team and lead the development of our next-generation Data Platform. This role is critical to ensuring that our data infrastruc [... source excerpt omitted ...] by creating intuitive abstractions, self‑service tooling, and clear documentation. Own and maintain core data pipelines and models that power internal dashboards, ML models, and customer-facing products. Own the Data & ML platform infrastructure using Terraform, including end‑to‑end administration of Databricks workspaces: manage user access, monitor performance, optimize configurations (e.g., clusters, lakehouse settings), and ensure high availability of data pipelines. Lead projects to improve data quality, testing, observability, and cost efficiency across existing pipelines and backend systems (e.g., migrating Databricks SQL pipelines to dbt, scaling data ingestion, impro