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January is a fintech company transforming consumer credit collections and recovery. The company has serviced over $20 billion in debt across 20+ million consumers, using AI to make financial hardship more human and recoverable. They are now expanding upstream across the entire consumer credit lifecycle.
As the first Data Systems Engineer, you will own the data foundation that powers every decision January makes about consumers. You'll architect and build the data infrastructure that currently supports collections operations and will scale to support all stages of consumer credit. This is a foundational role with significant autonomy: you'll decide what gets built, in what order, and how the data function interacts with downstream teams (engineers, analysts, ops, product).
Key responsibilities include:
- Building and owning end-to-end data pipelines that are reliable, scalable, and self-healing where possible
- Setting data quality standards and treating data quality as a key health indicator
- Designing systems for failure with safe replayability and clear recovery runbooks
- Migrating legacy systems into durable data pipelines, prioritizing highest-impact opportunities and making build-vs-buy decisions
- Collaborating with multiple stakeholders and data consumers, managing asks and holding appropriate boundaries
- Establishing ownership through writing and influence rather than authority, explaining technical tradeoffs to both engineers and non-technical stakeholders
- Architecting systems that leverage AI for self-repair and automation
- Building 0-to-1 and shaping the data function's structure and standards
The role operates within January's culture of decentralization, speed, candor, and AI-first thinking. You'll work in-person at least three days per week at their Nolita office in New York, with a hybrid model that emphasizes face-to-face collaboration and trust-building.
REQUIREMENTS:
- 4+ years of software engineering, data engineering, or data platforms experience
- Demonstrated ability to build and own data pipelines end-to-end
- Strong design for failure mindset: safe replayability, self-healing recovery, and clear runbooks
- Proven track record treating data quality as a key pipeline health indicator
- Experience migrating legacy systems and making build-vs-buy decisions
- Ability to collaborate with multiple stakeholders and manage competing asks
- Comfort drawing ownership lines through writing and influence
- Deep current use of AI in your own work, with interest in pushing AI further into systems architecture
- Passion for building 0-to-1 and shaping function design
