Data Architect Data Analytics

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. 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 Scale our data platform as we grow from dozens to hundreds of customers, anticipating needs before they become bottlenecks Have 3+ years of hands-on experience building production data pipelines using PythonKnow your way around SQL and modern cloud data warehouses; 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 itCare 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 Top-of-market salary and equity package~ based full-time employees)~ Medical, dental & vision insurance coverage for you~Equity~ Flexible PTO~