Senior Analytics Engineer
January · New York City
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.
About the Role
As January's Senior Analytics Engineer, you'll own the layer that makes our data trustworthy — for the people who use it today, and for the AI agents that will increasingly use it tomorrow. Data Engineering gets raw data reliably into Snowflake; you take it from there. You'll build and govern our semantic layer, standardize how teams across January define and measure success, and make sure that a metric means the same thing whether it's surfaced on a dashboard, in a Slack chatbot, or by an LLM answering a question on someone's behalf. You'll partner with Data Engineering to deliver impactful client reports, and you'll advocate for the data January needs to capture, but doesn't yet. This is a foundational hire for a company betting that the future of analytics is fewer people writing one-off queries and more trust built into the data itself.
You will:
Own the gold layer and build January's semantic layer — designing the dbt-driven, Snowflake-native layer that becomes the single source of truth for every tool that answers a data question, from Sigma to a Slack chatbot to future LLM-based interfaces
Define and enforce data contracts and standardized metrics — establishing clear ownership boundaries so gold-layer changes are intentional and communicated, and resolving cross-team disagreement about what a metric means
Partner on our client reporting revamp — working alongside Data Engineering (who own the underlying pipeline architecture) to clarify metric definitions, define client success criteria, and build the gold-layer models the new reporting experience needs — including data products clients don't know to ask for yet
Advocate to expand the data January captures — partnering with Analytics, Borrower Support, and Client Acquisition to close data-capture gaps (event granularity, structured conversational data, richer client attributes) that limit what your models can do
Own cost management for dbt, Snowflake compute powering the gold layer, and analytics tooling like Sigma
Enable trustworthy self-service — building certified, well-documented data products that let analysts, PMs, and ops teams (and eventually agents) get correct answers without pinging a data scientist
Deliver immediate impact through key projects, including:
Semantic Layer Buildout: Design and ship the first version of January's Snowflake-native semantic layer, feeding Sigma, internal tools, and future chatbot/LLM interfaces from a single certified source
Metrics Standardization: Resolve the highest-friction metric definition conflicts across teams and establish a durable process (a metrics registry or equivalent) to prevent recurrence
Client Reporting Revamp: Partner with Data Engineering to eliminate duplicated report logic and mismatched metric definitions across client reports
What We Need
Experience and Expertise:
5+ years in analytics engineering, data engineering, or a closely related analytics role
Deep expertise with a modern cloud data warehouse (Snowflake preferred)
Advanced SQL skills, with a track record of modeling data for both flexibility and trust
Experience designing, building, or governing a semantic layer (dbt Semantic Layer, Cube, LookML, or similar)
Proven ability to define metrics and data contracts that multiple teams actually adopt
Cross-Team Leadership:
A track record of walking into a room where teams disagree about what a metric means and leaving with one answer everyone uses
Experience partnering with data engineering or infrastructure teams on shared problems (like client reporting) without owning the whole stack yourself
History of building trust and adoption for self-serve data products, not just building them
Mindset and Approach:
Systems thinker who sees how a modeling decision ripples through dashboards, reports, and (increasingly) AI agents
Ownership mentality — comfortable with January's decentralized operating model, and willing to show ownership behavior beyond your formal remit when it serves the broader goal
Client-oriented — genuinely curious about what clients need from their data, not just what they ask for
Clear communicator who can write documentation people actually read and adopt
Bonus Points:
Experience building data products or context layers that also serve LLM-based or agentic consumers
Experience with a BI/self-serve tool such as Sigma or Looker
Background blending analytics engineering with client-facing or consulting work
Previous startup or high-growth company experience
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.