About Bonside

Capital is the bottleneck on local job creation

Small businesses create two-thirds of new American jobs and employ nearly half the private workforce — yet the cost to underwrite them locks good operators out of fair capital.

A laundromat owner with three profitable years and clean cash flow wants to open a second location. The $400k she needs is too small for banks to manually underwrite, so she's pushed to merchant cash advances at 40%+ effective rates that would kill her margin. The second location doesn't open. Six neighborhood jobs don't get created.

Multiply her by every restaurant, gym, salon, and fitness studio in the country: creditworthy B&M operators locked out of fair capital because the unit economics of evaluating them are upside down.

Both halves of the fix: agentic financing + the underwriting infrastructure beneath it

The financing business teaches us at the speed of real customers. The infrastructure — plugged into landlords, asset managers, and other capital deployers — gives us instant deal flow and lets us deploy partner capital into the businesses we underwrite. Each side compounds the other.

End state: the largest dataset of B&M creditworthiness in the world — fresh, accurate, queryable — and the ability to finance any B&M business at any time at a rate it actually deserves. Capital goes from months of negotiation to a moment's decision, and every good operator gets a fair shot at growing the business and the jobs around it.

Our right to win: distribution, data, and the moment of need

We've closed a $1.2T asset manager on the infrastructure side, we're live across 75+ properties, and we're pursuing embedded integrations into the moments operators need capital most — commercial real estate buildouts, tenant evaluation — where the pain is highest and the documents are already in motion. That's how we beat the unit economics math that's kept B&M financing stuck for decades. Underneath sits a proprietary B&M data layer: hundreds of thousands of normalized data points and specialized agents for extraction, normalization, risk evaluation, and pricing.

Staff data engineer

We're hiring a staff data engineer because in the next 18 months we're:

• Turning our batch underwriting pipelines into the real-time data layer that powers customer-facing product, partner APIs, and in-product underwriting feedback

• Scaling the data platform 5–10x in scope—more integrations, more models, more partners— without losing the performance, cost discipline, observability, and data contracts that keep underwriting decisions trustworthy

• Broadening the proprietary B&M creditworthiness dataset beyond accounting–POS, banking, CRE, extracted risk signals—so every deal is priced off the freshest, fullest picture available

• Pairing agentic extraction and normalization with rigorous evals, so the data flowing into pricing is trustworthy at the same pace it's growing

• Each is a 0-to-1 problem with founder-grade scope. You'll own a critical slice of the data platform end-to-end, ship it, and watch real businesses get funded—and create new jobs— because of what you built.

What you'll do

• Design and ship the data infrastructure that turns dozens of accounting, transactional, and operational sources into the freshest B&M creditworthiness dataset in the world

• Build the real-time data layer that takes underwriting out of overnight batch and into live product surfaces and partner workflows

• Partner with our AI engineers on agentic extraction, normalization, and document parsing– and design the evals that keep the underlying data trustworthy at scale

• Take prototypes to production with real attention to latency, cost, reliability, and data contracts

• Ship customer- and partner-facing features end-to-end, from warehouse model through the TypeScript surfaces operators actually use

What we're looking for

• Track record of building and commercially shipping 0-to-1 production data systems at scale

• End-to-end problem ownership; comfortable starting without a spec

• Deep fluency with modern data warehousing (Postgres, ClickHouse/Snowflake/BigQuery, dbt) and orchestration (Dagster, Airflow, Temporal, or similar)

• Strong Python and TypeScript–comfortable touching product code to ship data into the surfaces customers actually use

• Clear communicator on tradeoffs, risks, and decisions

Nice to have

• Hands-on experience with real-time/streaming data, data contracts, or observability for data systems at scale

• Experience pairing LLM extraction with deterministic data pipelines and eval frameworks

• Background in fintech, underwriting, accounting systems, or other operational financial data

Location

• NYC metro, hybrid with regular presence in our DUMBO office.