> Markdown version of [/jobs/ext/3786215-data-engineer](https://www.wearedevelopers.com/jobs/ext/3786215-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Engineer

- **Company:** Stuut, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Automation of Tests, BigQuery, Software as a Service, Cloud Engineering, Data Architecture, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, Python (Programming Language), Operational Databases, DataOps, SQL Databases, Snowflake, Integration Frameworks, Machine Learning Operations, Virtual Agents
- **Published:** October 16, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-engineer-san-francisco-ca--07b7da11-64c7-42f4-a2de-aaecd347379c

## About the Role

* Have 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
 * 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 elses stack
 * Have experience (or strong interest) in fintech, B2B SaaS, or financial data - understanding AR/AP workflows is a big plus

## Description

Stuut is transforming how B2B companies turn revenue into cash. Businesses worldwide have trillions tied up in receivables, yet the work between order and payment still relies on fragmented systems and labor-intensive manual processes. Stuut's AI agent works across order management, credit, collections, payments, cash application, disputes and deductions, carrying context through every step. Customers reduce DSO by 47%, collect 40% more cash and eliminate 70% of manual work without replacing their ERP.Stuut is already trusted by finance teams at companies including Honeywell, NCR and ZoomInfo, from Fortune 10 enterprises to scaling mid-market businesses. We are backed by a16z, Khosla Ventures, Activant, 1984 Ventures, Page One and Microsoft.The RoleTo build the data foundation that powers Stuuts intelligence layer. Youll 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, youll be our first data hire, which means youll 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. Youll translate messy data into clean, reliable datasets that drive product decisions, customer insights, and business growth. If youve ever wanted to own the entire data stack at a fast-growing company, this is it.What You'll DoBuild and own our data infrastructure from the ground up - design pipelines that ingest, transform, and model data from customer ERPs, payment processors, and internal systemsBuild the transformation and semantic layer that serves as the single source of metric truth across customer-facing analytics, internal reporting, and our AI/ML systemsDesign the canonical data model that normalizes information across heterogeneous source systems, with quality tests and observability built in from day oneBuild the event and signal pipelines that turn product interactions and outcomes into clean, labeled data - the foundation for analytics, ML, and intelligent product featuresPartner with product, engineering, and applied ML to embed data quality, lineage, and observability into everything we shipImplement DataOps best practices so our data - and the AI features built on top of it - stays timely, accurate, and trustedCollaborate with leadership to define KPIs, build dashboards, and surface insights that drive strategic decisionsScale our data platform as we grow from dozens to hundreds of customers, anticipating needs before they become bottlenecksYou Might Be a Fit If You...Have 3+ years of hands-on experience building production data pipelines using PythonKnow your way around SQL and modern cloud data warehouses; experience with Snowflake or BigQuery is a plusHave deep experience implementing ETL/ELT workflows at scale using tools like dbt, Airflow, or similar - and have opinions on what good looks likeHave built or contributed to a semantic / metrics layer and care about metric consistency across surfacesUnderstand data modeling fundamentals and can design canonical schemas that normalize messy, heterogeneous source data into something usableHave 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 concernsHave 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 elses stackHave experience (or strong interest) in fintech, B2B SaaS, or financial data - understanding AR/AP workflows is a big plusCompensationTop-of-market salary and equity packageBenefits (for U.S.-based full-time employees)Medical, dental & vision insurance coverage for you401(k) & MatchEquityFlexible PTOParental Leave