Collections 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 Role
As 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 Do
Own 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 features
Build 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 performance
Democratize data access — creating intuitive models that help PMs, analysts, and ops teams find answers independently while maintaining security and compliance requirements
Bridge 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 data
Institute modern data practices — implementing testing frameworks, building CI/CD pipelines for infrastructure changes, and creating documentation that enables others to extend your work
Drive strategic infrastructure decisions — identifying where new tools unlock capabilities, balancing quick wins with architectural vision, and building the foundation for an eventual data engineering team
Deliver immediate impact through key projects including:
Data Model Redesign: Architect unified models that reduce query redundancy for client reporting by 50% while maintaining flexibility
Pipeline Reliability: Strengthen monitoring systems to catch 99% of issues before they impact users
Cost Optimization: Reduce our Snowflake spend by 30-40% through intelligent clustering and lifecycle management
Analytics Enablement: Create semantic layers that enable technical and non-technical users alike to easily extract value from rich user data
What We're Looking For
Experience and Expertise:
5+ years in data engineering or analytics engineering with progressive technical responsibility
Deep expertise with modern data warehouses (Snowflake, BigQuery, or Redshift) including performance tuning and cost optimization
Advanced SQL skills — you can write elegant queries and debug why that 45-minute monster is destroying our compute budget
Production experience with dbt or similar transformation tools, including testing and documentation best practices
Proven ability to build and maintain ETL/ELT pipelines at scale using modern orchestration tools
Track record of designing data models that balance analytical flexibility with performance at scale
Technical Leadership:
Experience as a sole or lead data engineer, owning infrastructure end-to-end without a large team
History of partnering with engineering teams to improve data quality at the source
Demonstrated success in reducing infrastructure costs while improving performance
Experience implementing data quality frameworks and proactive monitoring systems
Mindset and Approach:
Systems thinker who sees beyond individual pipelines to understand organizational data flow
Ownership mentality — you build your own roadmap and drive initiatives without waiting for permission
Strategic perspective that connects technical decisions to business outcomes
Collaborative approach to working with analysts, engineers, and product managers
Clear communicator who writes documentation people actually read
Bias toward shipping iteratively rather than pursuing perfection
Bonus Points:
Experience with streaming architectures and real-time analytics
Familiarity with ML infrastructure and feature stores
Knowledge of financial data privacy regulations and compliance
Previous startup or high-growth company experience