---
title: 'Data Engineer at Stuut'
canonical: 'https://feeny.ai/job/data-engineer-stuut-san-francisco-nn0f7qzm77bs'
type: 'job'
last_seen: '2026-09-07'
---

# Data Engineer at Stuut

- **Company:** Stuut
- **Location:** San Francisco, CA
- **Compensation:** $135k–$190k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-03
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/stuut-ai/04251b6f-289a-4b8b-8980-166d9b91d148

## Job description

Stuut is transforming accounts receivable for B2B companies—making collections smarter and faster for companies that have historically relied on manual processes that are labor intensive and costly. Our platform is gaining traction with finance teams across industrials, chemicals, and manufacturing sectors from Fortune 10 brands to scaling midmarkets. We're backed by top-tier investors including a16z, Khosla, Activant, 1984 Ventures, Page One and Microsoft.

## 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 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 else's stack
- Have experience (or strong interest) in fintech, B2B SaaS, or financial data — understanding AR/AP workflows is a big plus

## Compensation

- Top-of-market salary and equity package
- Benefits (for U.S.-based full-time employees)
- Medical, dental & vision insurance coverage for you
- 401(k) & Match
- Equity
- Flexible PTO
- Parental Leave

## About Stuut

## Company Overview
- **One-liner**: Stuut provides an AI-powered platform that autonomously handles the entire accounts receivable (AR) process, from customer outreach to payment collection, to increase cash flow.
- **Entity Type**: Private (Series A)
- **Headquarters**: New York, New York, United States
- **Founded**: 2024
- **Founders**: Tarek Alaruri (CEO), Ben Winter (COO), Adam Chaarawi (Co-founder, Engineering)

## Core Business
- **Primary industries**: Accounts receivable automation, financial operations, order-to-cash software.
- **Target customers**: B2B – finance and accounting teams at companies using ERPs such as SAP, Oracle, NetSuite, and Dynamics (mid-market to enterprise).
- **Mission**: Transform accounts receivable from manual work into automated cash collection.

*Source: [stuut.ai](https://www.stuut.ai/), [stuut.ai/about](https://www.stuut.ai/about)*

## Products & Services
Stuut’s AI agent covers the full order-to-cash cycle with these modules:

- **Collections**: Proactively reaches out before invoices are overdue, finds billing contacts, and engages via email, SMS, and voice.
- **Cash Application**: Automatically matches incoming payments to invoices and provides click-to-pay functionality.
- **Deductions**: Autonomously identifies, investigates, and resolves deductions, recovering revenue that would otherwise be lost.
- **Disputes**: Handles disputes end-to-end – gathering documentation, communicating with customers, and maintaining detailed audit trails.
- **Payments**: Processes incoming payments and matches them to invoices.
- **Credit**: Evaluates customer creditworthiness and recommends optimal credit terms.

All modules operate on a unified AI that learns each customer’s payment patterns and preferences, personalizing every interaction. The platform integrates with major ERPs in 3–4 days.

*Source: [stuut.ai](https://www.stuut.ai/)*

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed.
- **Key Metrics**:
  - **Total Funding**: $29.5M (Series A led by Andreessen Horowitz, closed ~late 2025) – also $35.41M reported by CB Insights (possibly including prior rounds).
  - **Revenue**: Not publicly available.
  - **Employees**: 63 (as of mid-2026); monthly headcount growth of +11.1%, yearly growth of +212.5%.
  - **Active job postings**: 36 (monthly posting growth +56.5%, yearly +800%).
- **Notable Investors**: Andreessen Horowitz (lead, Series A), Khosla Ventures, Activant Capital (lead, Seed), 1984 Ventures, Carya Venture Partners, Page One Ventures.
- **Growth Signals**: 
  - Doubled headcount year-over-year.
  - Claims 40% average cash flow increase and 70% reduction in manual tasks for customers.
  - Linkedin followers grew 57.8% year-over-year to ~25,000.
  - Expanded from pilot to full product suite (6 modules) within 18 months of founding.

*Sources: [linkedin.com](https://www.linkedin.com/company/stuut), [cbinsights.com](https://www.cbinsights.com/company/stuut), [stuut.ai](https://www.stuut.ai/)*

## Competitive Advantages
- **True autonomy**: Unlike traditional AR software that provides better tools, Stuut’s AI agents execute complete workflows independently (end-to-end) without human oversight.
- **Rapid deployment**: 3–4 days vs. 6–18 months for legacy solutions.
- **Contextual intelligence**: Tracks every customer interaction across email, SMS, and voice, getting smarter over time.
- **Unified platform**: Collections, cash application, deductions, disputes, payments, and credit in one system – not a patchwork of point solutions.
- **ERP-native integration**: Works out-of-the-box with SAP, Oracle, NetSuite, Dynamics.

*Source: [stuut.ai](https://www.stuut.ai/)*

## Strategic Focus
- Continue building out the AI agent’s capabilities (e.g., adding more dispute and credit features).
- Scale the go-to-market team (open roles include Director of Revenue Operations, Product Marketing Manager) to capture the large AR automation market.
- Maintain rapid deployment and customer success to drive word-of-mouth growth.
- Deepen integrations with additional ERP systems and expand into adjacent financial workflows.

*Source: [linkedin.com](https://www.linkedin.com/company/stuut)*

## Why Work Here
- **Culture & values**: “Think independently,” intellectual curiosity, ownership, and proactive initiative. The team is described as operations and product nerds who hate bureaucracy.
- **Work model**: In-office in New York City (NYC headquarters). Built In lists “OnSite Workspace” with typical time on-site: None? (conflicting; but job postings mention hybrid). Likely a hybrid policy with strong in-office expectation.
- **Perks & benefits** (from Built In):
  - Healthcare benefits, childcare benefits, generous parental leave.
  - Company equity and performance bonuses.
  - Continuing education stipend, mentorship program, online course subscriptions.
  - Quarterly engagement surveys, diversity-focused ERGs, diversity recruitment program.
- **Growth opportunity**: High-growth startup (headcount up 212% YoY) with backing from top-tier VCs (a16z, Khosla). Employees get to shape the product and culture early.
- **Open roles**: 36 active positions across engineering (AI/ML, fullstack, data), product design, marketing, and revenue operations – sign of scaling.

*Sources: [builtin.com](https://builtin.com/company/stuut), [linkedin.com](https://www.linkedin.com/company/stuut)*

## Sources
1. [stuut.ai – Homepage](https://www.stuut.ai/)
2. [stuut.ai – About page](https://www.stuut.ai/about)
3. [linkedin.com – Stuut company page](https://www.linkedin.com/company/stuut)
4. [builtin.com – Stuut careers and perks](https://builtin.com/company/stuut)
5. [cbinsights.com – Stuut profile](https://www.cbinsights.com/company/stuut)

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