Collections RevolutionCollections runs like an emergency room. You show up in crisis, get triaged by a stranger who doesn't know your history and leave with no follow-up. We're turning it into primary care for consumer finance. We started in the hardest, most broken stage, because if it works there it works anywhere. 70 million Americans fall behind on a debt every year.
Most want to pay what they owe and can't find a way back. 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. They rate us about 50% higher than the banks that lent them the money. Creditors net over 30% more with us because we collect more and charge less.
Most AI strips the human out of the work. We use it to do the opposite. In someone's hardest financial moment we make the experience 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 one in collections today is the one who gets approved tomorrow.About the RoleAs January's founding Senior Data Engineer, you'll transform how we leverage data to expand access to credit — not by fixing what's broken, but by unlocking what's possible. You'll take full ownership of our modern data stack, evolving it from a capable system maintained part-time by analysts and engineers into a world-class platform that anticipates and enables our most ambitious data initiatives. You'll design the data infrastructure that helps millions achieve financial stability, ensuring every insight flows seamlessly from production to decision-makers. By establishing data engineering as a core discipline at January, you'll free our analysts to focus on insights while you architect the scalable foundation that powers our next phase of growth.What You'll DoOwn and optimize our entire data platform — taking our Snowflake warehouse from analyst-maintained to engineer-optimized while standardizing data models for customer reporting, operational dashboards, and ML featuresBuild self-healing data pipelines — designing ETL processes that scale automatically with volume, implementing monitoring that catches issues before anyone notices, and optimizing costs without sacrificing performanceDemocratize data access — creating intuitive models that help PMs, analysts, and ops teams find answers independently while maintaining security and compliance requirementsBridge engineering and analytics — establishing feedback loops between production systems and analytical needs, ensuring schema changes don't break downstream dependencies, and influencing how new features generate dataInstitute modern data practices — implementing testing frameworks, building CI/CD pipelines for infrastructure changes, and creating documentation that enables others to extend your workDrive strategic infrastructure decisions — identifying where new tools unlock capabilities, balancing quick wins with architectural vision, and building the foundation for an eventual data engineering teamDeliver immediate impact through key projects including: Data Model Redesign: Architect unified models that reduce query redundancy for client reporting by 50% while maintaining flexibilityPipeline Reliability: Strengthen monitoring systems to catch 99% of issues before they impact usersCost Optimization: Reduce our Snowflake spend by 30-40% through intelligent clustering and lifecycle managementAnalytics Enablement: Create semantic layers that enable technical and non-technical users alike to easily extract value from rich user dataWhat We're Looking ForExperience and Expertise:5+ years in data engineering or analytics engineering with progressive technical responsibilityDeep expertise with modern data warehouses (Snowflake, BigQuery, or Redshift) including performance tuning and cost optimizationAdvanced SQL skills — you can write elegant queries and debug why that 45-minute monster is destroying our compute budgetProduction experience with dbt or similar transformation tools, including testing and documentation best practicesProven ability to build and maintain ETL/ELT pipelines at scale using modern orchestration toolsTrack record of designing data models that balance analytical flexibility with performance at scaleTechnical Leadership:Experience as a sole or lead data engineer, owning infrastructure end-to-end without a large teamHistory of partnering with engineering teams to improve data quality at the sourceDemonstrated success in reducing infrastructure costs while improving performanceExperience implementing data quality frameworks and proactive monitoring systemsMindset and Approach:Systems thinker who sees beyond individual pipelines to understand organizational data flowOwnership mentality — you build your own roadmap and drive initiatives without waiting for permissionStrategic perspective that connects technical decisions to business outcomesCollaborative approach to working with analysts, engineers, and product managersClear communicator who writes documentation people actually readBias toward shipping iteratively rather than pursuing perfectionBonus Points:Experience with streaming architectures and real-time analyticsFamiliarity with ML infrastructure and feature storesKnowledge of financial data privacy regulations and compliancePrevious startup or high-growth company experience