About the job
At January, we are revolutionizing the credit landscape. Our innovative, data-driven platform is designed to restore trust, achieve tangible results, and empower millions to progress toward a brighter financial future, while also humanizing the consumer finance experience. By harnessing data intelligence, we strive to build trust and deliver improved outcomes for both consumers and creditors.
Our mission is straightforward: to broaden access to credit while equipping consumers with the tools they need to attain long-lasting stability and control over their financial lives. We initiated our journey by creating a robust foundation for creditors to engage and support borrowers throughout the entire debt lifecycle. By merging exceptional performance with unique consumer satisfaction and strict compliance, we have perfected outsourced collections. Our journey is just beginning, and together we are forging a financial system where trust and opportunity catalyze enduring change in people's lives.
About the Role
As the pioneering Senior Data Engineer at January, you will redefine our approach to data utilization in expanding access to credit—not merely by fixing existing issues but by unlocking new possibilities. You will take complete ownership of our modern data stack, transforming it from a system supported sporadically by analysts and engineers into a premier platform that anticipates and facilitates our most ambitious data initiatives. You will architect the data infrastructure that empowers millions to achieve financial stability, ensuring that insights flow seamlessly from production to decision-makers. By establishing data engineering as a core discipline at January, you will allow our analysts to focus on insights while you construct the scalable foundation that propels our next growth phase.
What You'll Do
Own and enhance our comprehensive data platform — transforming our Snowflake warehouse from analyst-managed to engineer-optimized while standardizing data models for customer reporting, operational dashboards, and machine learning features.
Create robust, self-healing data pipelines — designing ETL processes that scale automatically with data volume, implementing monitoring systems that preemptively identify issues, and optimizing costs without compromising performance.
Facilitate accessible data utilization — developing intuitive models that empower PMs, analysts, and operations teams to independently find answers while adhering to security and compliance standards.
Integrate engineering with analytics — establishing feedback mechanisms between production systems and analytical demands, ensuring schema changes do not disrupt downstream dependencies, and influencing how new features generate data.
Lead data initiatives — championing projects that enhance our data capabilities, contributing to strategic data-driven decisions, and mentoring junior engineers.

