Senior Analytics Engineer

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. **At January, we're fixing what's broken in credit.** Our data-driven platform rebuilds trust, delivers results, and helps millions move toward brighter financial futures while bringing humanity to consumer finance. Using data intelligence, we create trust and deliver better outcomes for consumers and creditors alike. Our mission is simple: expand access to credit while empowering consumers to achieve lasting stability and control of their financial lives. We began by building the foundation for creditors to engage with and support their borrowers at scale across the entire debt lifecycle. We've mastered outsourced collections by combining best-in-class performance with differentiated consumer satisfaction and superior compliance. And we're just getting started. Together, we're creating a financial system where trust and opportunity spark lasting change in people's lives. ## 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. ## What You'll Do - **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're Looking For **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 ## **We are currently hiring for this position in our New York office.** 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.