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.
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
Experience and Expertise:
Qualification
Decentralization beats controlSpeed beats perfectionCandor beats comfortWriting beats the average meeting
Preferred
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
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.
