Data Engineer

Stuut-ai is transforming accounts receivable for B2B companies, and they are seeking a Data Engineer to build the data foundation that powers their intelligence layer. This role involves designing and building data infrastructure, transforming raw financial data into actionable insights, and collaborating with product and engineering teams to drive business growth.

Responsibilities:

• 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

Requirements:

• 3+ 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
• Deep experience implementing ETL/ELT workflows at scale using tools like dbt, Airflow, or similar — and have opinions on what good looks like
• 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
• 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
• Thrive in ambiguity and get energized by building something new rather than inheriting someone else's stack
• Experience partnering with ML or applied AI teams on feature pipelines or supporting data infrastructure (bonus, not required)
• Experience (or strong interest) in fintech, B2B SaaS, or financial data — understanding AR/AP workflows is a big plus