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