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January is a fintech platform transforming consumer credit collections and lending. The company has serviced over $20 billion in debt across 20+ million consumers, combining AI with human-centered design to improve outcomes for both borrowers and creditors.
As Senior Analytics Engineer, you will own the semantic layer and data governance infrastructure that makes January's data trustworthy for both human analysts and AI agents. You'll design and build a dbt-driven, Snowflake-native semantic layer serving as the single source of truth across all analytics tools—from Sigma dashboards to Slack chatbots to future LLM-based interfaces.
Key responsibilities include:
- Design and ship January's semantic layer, establishing a certified data foundation that feeds multiple downstream tools and interfaces
- Define and enforce data contracts and standardized metrics across teams, resolving cross-functional disagreements about metric definitions
- Partner with Data Engineering on client reporting revamp, clarifying metric definitions and building gold-layer models that deliver new data products
- Advocate for expanded data capture by working with Analytics, Borrower Support, and Client Acquisition teams to close gaps in event granularity, conversational data, and client attributes
- Own cost management for dbt, Snowflake compute, and analytics tooling like Sigma
- Enable self-serve analytics by building certified, well-documented data products that let analysts, PMs, and ops teams get correct answers independently
- Establish a durable metrics registry or equivalent process to prevent metric definition conflicts
You'll need 5+ years in analytics engineering, data engineering, or related analytics roles. Deep expertise with Snowflake (or modern cloud data warehouses), advanced SQL skills, and proven experience designing or governing semantic layers (dbt Semantic Layer, Cube, LookML, or similar) are essential. You should have a track record of defining metrics and data contracts that teams actually adopt, walking into disagreements and leaving with unified definitions everyone uses.
This is a foundational hire for a company betting that the future of analytics is fewer one-off queries and more trust built into data itself. You'll work cross-functionally with Data Engineering, Analytics, Product, and Operations teams, building adoption for self-serve data products while maintaining a systems-thinking approach to how modeling decisions ripple through dashboards, reports, and AI agents.
