Data Engineer

About Stuut

Stuut is the AI coworker that runs order-to-cash for the world’s leading enterprises. Across order management, credit, collections, payments, cash application, disputes and deductions, Stuut does the work end to end and keeps going until the cash hits the bank.

Over 150 companies, spanning the mid-market to the Fortune 50, including Honeywell, ZoomInfo and PerkinElmer, use Stuut to unlock up to 40% more cash flow and cut DSO by 47%. More than $3 billion has moved through the platform to date.

Founded in New York by Tarek Alaruri and Ben Winter, Stuut has raised $93 million from Insight Partners, Andreessen Horowitz, M12, Microsoft’s Venture Fund, and others. Learn more at stuut.ai.

The Role

To build the data foundation that powers Stuut's intelligence layer. You'll work closely with our product and engineering teams to transform raw financial data into actionable insights that help our customers get paid faster. This is a foundational role, you'll be our first data hire, which means you'll shape everything from our data architecture to how we think about analytics.

This is a high-impact role for someone who can think strategically about data infrastructure while rolling up their sleeves to build pipelines, models, and systems from scratch. You'll translate messy data into clean, reliable datasets that drive product decisions, customer insights, and business growth. If you've ever wanted to own the entire data stack at a fast-growing company, this is it.

What You’ll Do

• Build and own our data infrastructure from the ground up — design pipelines that ingest, transform, and model data from customer ERPs, payment processors, and internal systems
• Build the transformation and semantic layer that serves as the single source of metric truth across customer-facing analytics, internal reporting, and our AI/ML systems
• Design the canonical data model that normalizes information across heterogeneous source systems, with quality tests and observability built in from day one
• Build the event and signal pipelines that turn product interactions and outcomes into clean, labeled data — the foundation for analytics, ML, and intelligent product features
• Partner with product, engineering, and applied ML to embed data quality, lineage, and observability into everything we ship
• Implement DataOps best practices so our data — and the AI features built on top of it — stays timely, accurate, and trusted
• Collaborate with leadership to define KPIs, build dashboards, and surface insights that drive strategic decisions
• Scale our data platform as we grow from dozens to hundreds of customers, anticipating needs before they become bottlenecks
• You Might Be a Fit If You…
• Have 5+ years of hands-on experience building production data pipelines using Python
• Know your way around SQL and modern cloud data warehouses; experience with Snowflake or BigQuery is a plus
• Have deep experience implementing ETL/ELT workflows at scale using tools like dbt, Airflow, or similar — and have opinions on what good looks like
• Have built or contributed to a semantic / metrics layer and care about metric consistency across surfaces
• Understand data modeling fundamentals and can design canonical schemas that normalize messy, heterogeneous source data into something usable
• Have worked with real-world data from SaaS APIs, ERPs, and third-party integrations — and have battle scars to show for it
• Care deeply about data quality and observability — freshness, lineage, automated testing, and anomaly detection as first-class concerns
• Have experience partnering with ML or applied AI teams on feature pipelines or supporting data infrastructure (bonus, not required)
• Thrive in ambiguity and get energized by building something new rather than inheriting someone else's stack
• Have experience (or strong interest) in fintech, B2B SaaS, or financial data — understanding AR/AP workflows is a big plus
• US Compensation
• Top-of-market salary and equity package