At January, we're fixing what's broken in credit. Our data-driven platform rebuilds trust, delivers results, and helps millions move toward brighter financial futures while bringing humanity to consumer finance. Using data intelligence, we create trust and deliver better outcomes for consumers and creditors alike.

Our mission is simple: expand access to credit while empowering consumers to achieve lasting stability and control of their financial lives. We began by building the foundation for creditors to engage with and support their borrowers at scale across the entire debt lifecycle. We've mastered outsourced collections by combining best-in-class performance with differentiated consumer satisfaction and superior compliance. And we're just getting started. Together, we're creating a financial system where trust and opportunity spark lasting change in people's lives.

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