Job description

Collections today works like an emergency room. The doctors carry too many patients, everyone arrives at their worst moment, and nobody has their chart. We're making it primary care. January personalizes interactions and optimizes decisions across every stage of consumer credit. We started in the hardest, most broken stage, because if it works there it works anywhere. Most consumers want to pay what they owe. They want a way out, not a break. We've serviced over $20 billion in debt across more than 20 million consumers. We see more people with charged-off loans each year than all but the top five US banks. Those consumers rate us about 50% higher than the banks that lent them the money. Creditors net over 30% more because we collect more and charge less. Most AI strips the human out of the work. We use it to make someone's hardest financial moment more human. The more human we make it, the more people recover. Now we're moving upstream, catching people before they default and building across every stage of the consumer credit lifecycle. The consumer in collections today is the consumer who gets approved tomorrow. As our first Data Systems Engineer, you'll own the foundation under every decision we make: what data lands, what our reports say, and what we offer to consumers. When the data is right, someone in their hardest financial moment gets an offer that fits their life. When it's wrong, they get the wrong balance, the wrong call, or nothing at all. ABOUT THE ROLE We're hiring our first data systems engineer. Data is the most valuable thing January has: 20 million consumers, $20 billion in debt, and every record behind them. Every decision we make about a consumer starts with our data. As we grow our business, it’s critical that our data system scales with it. The data that powers collections now has to power every stage of consumer credit. You'll decide what that foundation looks like and how it grows. This is a blank page. What gets built, in what order, and how this function interacts with downstream consumers is yours to decide. You'll set the standard for how we treat our data: trusted by default, understood by everyone who touches it, and increasingly able to look after itself. Engineers, analysts, ops, and product all depend on what you build. You'll set the standards that let them make good calls on data without you in the room. You thrive on obsessing over data quality and infrastructure, and you move fast to improve both. WHAT WE NEED - You've built and owned a data pipeline end to end. - You design for failure. Safe replayability, recovery that self heals where possible and has a clear runbook when it doesn’t - You treat data quality as a key indicator of pipeline health - You can own migrating a legacy system into a durable data pipeline by identifying the highest impact opportunities first, and you know when to build vs. buy. - You've collaborated with multiple stakeholders/data consumers, with an ability to manage asks and hold boundaries where they need to be held - You draw ownership lines through writing and influence instead of authority, and explain tradeoffs equally well to engineers and non-engineers. - You already use AI deeply in your own work and want to go further, including by architecting systems that repair themselves. - You love building 0 to 1, and you want the shape of a function to be yours. - 4+ years in software engineering, data engineering or data platforms. HOW WE WORK - Decentralization beats control. The best calls get made by the people closest to them, not routed up a chain. You'll set the standards that let the team decide without you in the room. - Speed beats perfection. You run tight loops, act at 70% on reversible calls and adjust as you learn. Fast loops beat slow ones. - Candor beats comfort. You'd rather hear a hard truth early than a polite sidestep that wastes everyone's time. - Writing beats the average meeting. Clarity scales. - AI runs through everything here. 95% of our consumer interactions are AI-driven. Internally, we've built an LLM code reviewer and approver. We benchmark coding agents against real tickets so we can automate more of them. Over the last six months, Engineering has doubled how much we can ship. Non-technical teams are just as fluent in AI - anyone can spin up a custom LLM Slackbot, and everyone consistently builds AI tools and skills that are shipped org-wide. - We operate at every altitude. No one here lives only on Mount Olympus, not even the leaders. We get into the trenches to learn the ground truth. We constantly refine our information flows so that our decisions are driven by the best, most up to date inputs. - We build in person, at least three days a week in our office in Nolita, with a growing group coming in every day. Random run-ins cross-pollinate ideas. Face time builds trust no thread can. Building alongside people makes work far more fun. As a New York City-based company, we are dedicated to transparent, fair, and equitable compensation practices that reflect our commitment to fostering an environment where all team members are valued and supported. We encourage individuals from all backgrounds to apply. We are an equal opportunity employer committed to diversity and inclusion in the workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, age, veteran status, or any other legally protected characteristic.

Requirements

Must-haves

Engineers, analysts, ops, and product all depend on what you build. You'll set the standards that let them make good calls on data without you in the room.
You thrive on obsessing over data quality and infrastructure, and you move fast to improve both.
You've built and owned a data pipeline end to end.
You design for failure. Safe replayability, recovery that self heals where possible and has a clear runbook when it doesn’t
You treat data quality as a key indicator of pipeline health
You can own migrating a legacy system into a durable data pipeline by identifying the highest impact opportunities first, and you know when to build vs. buy.
You've collaborated with multiple stakeholders/data consumers, with an ability to manage asks and hold boundaries where they need to be held
You draw ownership lines through writing and influence instead of authority, and explain tradeoffs equally well to engineers and non-engineers.
You already use AI deeply in your own work and want to go further, including by architecting systems that repair themselves.
You love building 0 to 1, and you want the shape of a function to be yours.
4+ years in software engineering, data engineering or data platforms.
Decentralization beats control. The best calls get made by the people closest to them, not routed up a chain. You'll set the standards that let the team decide without you in the room.
Speed beats perfection. You run tight loops, act at 70% on reversible calls and adjust as you learn. Fast loops beat slow ones.
Candor beats comfort. You'd rather hear a hard truth early than a polite sidestep that wastes everyone's time.
Writing beats the average meeting. Clarity scales.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigollm

Platform: ashby
Effort: quick