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Data Engineer

San Francisco · New York City · San Francisco, California, United States

Open 5 months, first seen by us 164 days ago
Pay
Pay not listedSimilar roles state $127K–$190K
Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
Engineering — team source
Department: Engineering
From the employer’s posting

What you’ll work on

Full posting
  • Design pipelines that ingest and model data from customer ERPs, payment processors and internal systems

  • Build the transformation and semantic layer as the single source of metric truth

  • Design a canonical data model that normalizes heterogeneous source systems

  • Build event and signal pipelines that produce clean, labeled data for analytics and ML

  • Implement DataOps practices for timely, accurate, trusted data

From the employer’s posting
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
Design the canonical data model that normalizes information across heterogeneous source systems
Build the event and signal pipelines that turn product interactions and outcomes into clean, labeled data
Implement DataOps best practices so our data — and the AI features built on top of it — stays timely, accurate, and trusted

About Stuut

Stuut is the AI coworker that runs order-to-cash for the world’s leading enterprises.

In the employer’s words · Read in context · Company website ↗

Apply at Stuut

Posting record

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Open 5 monthsWe first saw this posting 164 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
Jun 2, 2026164 days ago
Recorded sightings
82
Last seen by us
Nov 13, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$127K–$190K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 2,239 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 164

Up longer than most. Similar jobs usually come down between day 6 and day 56.

From 13,630 similar postings that came down. Coming down is not always a hire.

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • BigQuery
  • dbt
  • Snowflake
  • Airflow
Where the posting names them
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 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
- 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

Your next step

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Demand for Data Engineer roles

New Data Engineer postings at the employers we follow are up 7% over the last 6 weeks compared with the 6 before.

Weekly new Data Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 860, the most 1,354, and the latest week had 1,335.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025
$139,500
Expected change, 2025 to 2035
9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

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

  • Benefits (for U.S.-based full-time employees)

  • Medical, dental & vision insurance coverage for you

  • 401(k) & Match

  • Equity

  • Flexible PTO

  • Parental Leave

Stuut is an equal opportunity employer committed to building a team with diverse backgrounds, perspectives, and experiences. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, genetic information, or any other characteristic protected by applicable law.

We are also committed to providing reasonable accommodations throughout the application and interview process. If you need any accommodations, please do not hesitate to reach out.

Source details

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Location & working pattern

San Francisco, California, United States

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