Senior Analytics Engineer

**January** Senior Analytics Engineer; $180K – $211.5K + Equity; New York City (Hybrid)

**About Company** At January, we're rebuilding one of the most broken parts of consumer finance: what happens after someone falls behind on debt. Our autonomous, compliant collections platform works directly for creditors, using data and AI to personalize outreach and offer consumers clearer, more compassionate ways to resolve what they owe. Creditors recover more effectively with less risk; consumers get dignity and control during a distressing situation.

January has serviced $20B+ in debt and engaged 20M+ consumers. We outperform traditional agencies by 30%+ for 80%+ of our clients, consumer satisfaction is ~50% higher than at the largest banks, and the product is roughly 300x more efficient than traditional collection agencies. Customers like Affirm and LendingClub stay and expand. We're now growing beyond late-stage debt collection into a multi-product platform, backed by Series B funding, with ~100+ people working out of Nolita, NYC.

**About the Role** January is hiring a founding-level Senior Analytics Engineer to own everything from the gold layer onward: semantic layers, metric definitions, data contracts, and cross-team metrics standardization. Data Engineering owns raw ingestion and warehouse reliability (bronze to silver); this person picks up from there and makes sure a metric like "recovery rate" means the same thing whether it's queried in a BI tool, a Slack chatbot, or eventually an LLM agent. It's as much a governance and cross-team influence role as a technical one.

**Responsibilities** - Own the gold layer and build January's Snowflake-native, dbt-driven semantic layer, feeding Sigma, internal tools, and future chatbot/LLM interfaces from a single certified source. - Define and enforce data contracts and standardized metrics, resolving cross-team disagreement about what a metric means. - Partner with Data Engineering on the client reporting revamp, clarifying metric definitions and building the gold-layer models the new reporting experience needs. - Advocate to expand the data January captures, partnering with Analytics, Borrower Support, and Client Acquisition to close data-capture gaps. - Own cost management for dbt, Snowflake compute powering the gold layer, and analytics tooling like Sigma. - Build certified, well-documented data products that let analysts, PMs, ops teams, and eventually agents get correct answers without pinging a data scientist.

**Required Skills** A. 5+ years in analytics engineering, data analytics, or a closely related analytics role — data engineering experience is a nice-to-have, not the primary background. B. Advanced SQL, explicitly evidenced in the resume or work history — non-negotiable. C. Track record owning or leading metric/data-modeling work as the sole or lead analytics engineer, not just contributing to a larger, DE-owned pipeline. D. Experience defining metrics or data contracts that multiple teams actually adopted. E. Experience navigating and resolving cross-team disagreement about metric definitions.

**Bonus Skills** - dbt experience strongly preferred; ETL familiarity (Airbyte, Fivetran, or similar) is a plus. - Named experience with a semantic layer tool (dbt Semantic Layer, Cube, LookML, Looker). - Experience with a BI/self-serve tool such as Sigma or Looker. - Experience building data products or context layers that also serve LLM-based or agentic consumers (structured context for chatbots, MCP-style interfaces). - Background blending analytics engineering with client-facing or consulting work. - Direct experience with Snowflake/dbt cost optimization. - Previous startup or high-growth company experience. - Master's degree in a relevant discipline. - General AI experience is a plus.

**Logistical Info** - Location: New York City, hybrid (in-office days consistent with January's standard policy). - Compensation: $180K – $211.5K base + Equity. - **Other:** Offers equity. Visa/relocation handled case-by-case, consistent with January's standard eng hiring practice.

**Ideal Background** - Founding analytics engineer or data engineer at a company or high-growth startup, or has led a team as a manager but is open to doing IC work. - Analytics/data engineering background at a high-caliber company with a modern data stack (Snowflake + dbt). - Direct experience owning a semantic layer or metrics layer, not just building transformation pipelines. - Comfortable operating cross-functionally, with a track record of resolving metric-definition conflicts across teams rather than just building models in isolation. - Master's degree in a relevant discipline is a plus.

**Green Flags** - Systems thinker who sees how a modeling decision ripples through dashboards, reports, and AI agents. - Ownership mentality, showing initiative beyond formal remit in a decentralized operating environment. - Client-oriented, genuinely curious about what clients need from data, not just what they ask for. - Clear, documentation-first communicator (writing-first engineering culture). - Experience building data products for LLM/agentic consumers, not just BI dashboards. - AI/LLM experience, even informal, is a plus given the direction of this role.

**Red Flags** - Data-engineer-first resumes — heavy on orchestration/infrastructure keywords, light on SQL/dbt fluency. Per January, these are a better fit for their separate Data Engineering pipeline; do not route here. - Heavy specialized/academic credentials (e.g., PhDs) with limited hands-on production experience. - Background limited to one-off SQL queries or ad hoc reporting, without semantic layer or data contract ownership. - No experience navigating cross-team disagreement over metric definitions.

**Interview Process** 1. Recruiter Screen 2. Hiring Manager Screen 3. Technical Screen 4. HM Check-in 5. Panel Round 6. Executive Interview