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Founding Data Engineer

Reads like an Analytics Engineer role: dbt models and clean tables.

Pay
Pay not listedSimilar roles state $122K–$190K
Work setting
On-site — work setting source
Work setup in the employer’s posting: On-site
From the employer’s posting
Employment
Full-time — employment source
Employment type in the employer’s posting: FullTime
From the employer’s posting

What you’ll work on

Full posting

Braintrust is growing quickly across enterprise and self-serve customers, and our internal systems need to scale with us.

The core of the job is analytics engineering.

  • Define business entities and metrics that Product, Finance, and GTM build on

  • Design and build core data models in SQL and dbt

  • Build and own ingestion from product events, CRM, and other business systems

  • Stand up self-serve analytics with semantic models and trusted metrics

  • Make architectural decisions for storage, orchestration, and transformation

From the employer’s posting
Braintrust is growing quickly across enterprise and self-serve customers, and our internal systems need to scale with us. You will establish our data foundation, make architectural decisions, work directly with our executive team, and help shape the data function as it grows. This is a founding, hands on role. You will decide what good looks like, and be able to build it.
The core of the job is analytics engineering. That means defining the business entities and metrics the company runs on, modeling them so they can be trusted, and making them accessible without a data person in the loop. Around that core, you will own the ingestion pipelines that feed it, the architecture decisions that hold it together, and the reliability of the whole path.
Define the business entities, metrics, and semantics that Product, Finance, and GTM all build on
Design and build the core data models in SQL and dbt
Build and own ingestion from product events, CRM, and other business systems
Stand up self-serve analytics that includes building semantic models and trusted metrics
Make architectural decisions by determining how storage, orchestration, and transformation evolve from here

What you’ll bring

All qualifications

Core experience

  • Experience defining business entities and metrics and building their models
  • Strong command of SQL and dbt
  • Cloud warehouse experience (Snowflake or equivalent) and an orchestrator (Dagster, Airflow, or similar)
  • Able to write and maintain production code, not only transformation models
  • Comfortable working as the only data hire with executives
Qualification wording
You've defined business entities and metrics, and built the models behind them
Strong command of SQL and dbt
experience with a cloud warehouse (Snowflake or equivalent) and an orchestrator (Dagster, Airflow, or similar)
You can write and maintain production code, not only transformation models.
Comfort operating as the only data hire, balancing foundational architecture against urgent business needs

About Braintrust

Braintrust is the agent observability platform.

In the employer’s words · Read in context

Apply at Braintrust

Posting record

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Open 4 monthsWe first saw this posting 145 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 19, 2026145 days ago
Recorded sightings
77
Last seen by us
Nov 11, 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

$122K–$190K is typical

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

From 331 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 145

Up longer than most. Similar jobs usually come down between day 7 and day 51.

From 1,613 similar postings that came down. Coming down is not always a hire.

Braintrust is hiring

1 open job

  • No new data jobs since June
Hiring record

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.

  • SQL
  • AWS
  • dbt
  • Fivetran
  • Looker
  • Snowflake
  • Terraform
  • Airflow
  • Dagster
Where the posting names them
- Define the business entities, metrics, and semantics that Product, Finance, and GTM all build on - Design and build the core data models in SQL and dbt - Build and own ingestion from product events, CRM, and other business systems, including vendor APIs, webhooks, and sync gaps
- Ingestion: Product events, CRM, and other business data sources - Modeling: SQL, dbt, business entities, and shared metric definitions - Storage and orchestration: The current environment includes Snowflake; you’ll help determine how the architecture evolves
- You've defined business entities and metrics, and built the models behind them - Strong command of SQL and dbt, and experience with a cloud warehouse (Snowflake or equivalent) and an orchestrator (Dagster, Airflow, or similar) - Enough engineering depth to own pipelines end to end. You can write and maintain production code, not only transformation models.
- Familiarity with usage-based pricing, consumption models, or PLG-plus-sales-assisted funnels - Experience with our stack, or close equivalents: Snowflake, dbt, Dagster, AWS Batch/Lambda, Fivetran, Terraform, GitHub Actions, Soda, or semantic-layer tools like Omni or Looker - You've built or used agents for internal workflows and analysis
- Modeling: SQL, dbt, business entities, and shared metric definitions - Storage and orchestration: The current environment includes Snowflake; you’ll help determine how the architecture evolves - Analytics: Semantic models and self-serve access to trusted metrics

Your next step

Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.

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Demand for Analytics Engineer roles

New Analytics Engineer postings at the employers we follow are down 14% over the last 6 weeks compared with the 6 before.

Weekly new Analytics Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 93, the most 174, and the latest week had 93.
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 Analytics Engineer, by the work the posting describes.

Analytics engineers turn raw warehouse data into clean, tested and documented tables that analysts and dashboards can rely on, often with dbt.

Expect strong SQL, data modeling, Git, and a warehouse such as Snowflake, BigQuery or Redshift.

A data engineer gets data into the warehouse; an analytics engineer shapes it once it is there and works closer to the analysts who use it.

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 analytics engineers, so these figures are for the closest group, database architects, the same group we use for data engineers.

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 the company

Braintrust is the agent observability platform. By actively applying intelligence to agent traces and automatically surfacing the most critical patterns, Braintrust gives teams the visibility to understand how agents behave in production and the tools to improve them.

Teams at Notion, Stripe, Box, OpenAI, and Cloudflare use Braintrust to trace their agents, find the issues in their observability data, and run evals that tell them how to improve.

About the Role

Braintrust is growing quickly across enterprise and self-serve customers, and our internal systems need to scale with us. You will establish our data foundation, make architectural decisions, work directly with our executive team, and help shape the data function as it grows. This is a founding, hands on role. You will decide what good looks like, and be able to build it.

The core of the job is analytics engineering. That means defining the business entities and metrics the company runs on, modeling them so they can be trusted, and making them accessible without a data person in the loop. Around that core, you will own the ingestion pipelines that feed it, the architecture decisions that hold it together, and the reliability of the whole path.

You'll partner with stakeholders across the business, and directly with executives. You will work with Product and Engineering on usage data and event schemas, with Finance on revenue and consumption reporting, and with Sales, RevOps, and Marketing on the pipeline and customer data they operate from.


What You Will Do

  • Define the business entities, metrics, and semantics that Product, Finance, and GTM all build on

  • Design and build the core data models in SQL and dbt

  • Build and own ingestion from product events, CRM, and other business systems, including vendor APIs, webhooks, and sync gaps

  • Make architectural decisions by determining how storage, orchestration, and transformation evolve from here

  • Stand up self-serve analytics that includes building semantic models and trusted metrics that stakeholders can query without filing a ticket

  • Partner with Engineering and Product on usage data and event schemas as the product surface grows

  • Treat governance and reliability as part of the data model

  • Use AI to accelerate your own build, and keep the foundation clean and well-described enough that AI tooling on top of it has something reliable to stand on.

Where You’ll Work in the Stack:

  • Ingestion: Product events, CRM, and other business data sources

  • Modeling: SQL, dbt, business entities, and shared metric definitions

  • Storage and orchestration: The current environment includes Snowflake; you’ll help determine how the architecture evolves

  • Analytics: Semantic models and self-serve access to trusted metrics

  • Governance and reliability: Data quality, access controls, lineage, monitoring, and debugging

About You

  • You've defined business entities and metrics, and built the models behind them

  • Strong command of SQL and dbt, and experience with a cloud warehouse (Snowflake or equivalent) and an orchestrator (Dagster, Airflow, or similar)

  • Enough engineering depth to own pipelines end to end. You can write and maintain production code, not only transformation models.

  • Clear judgment about data modeling, schema evolution, contracts, and lineage

  • Strong debugging instincts across a multi-system path, and the patience to trace a wrong number to its source

  • Ability to work directly with executives and functional leaders: clarifying vague requests, explaining how the data works, and negotiating priorities and tradeoffs

  • Comfort operating as the only data hire, balancing foundational architecture against urgent business needs

Bonus Points

  • You've built a data foundation from scratch, rather than inheriting a mature one

  • Startup experience, especially as an early or first data hire

  • Experience across multiple business functions that includes Product, Finance, and GTM

  • Familiarity with usage-based pricing, consumption models, or PLG-plus-sales-assisted funnels

  • Experience with our stack, or close equivalents: Snowflake, dbt, Dagster, AWS Batch/Lambda, Fivetran, Terraform, GitHub Actions, Soda, or semantic-layer tools like Omni or Looker

  • You've built or used agents for internal workflows and analysis

  • Braintrust user :)

What Success Looks Like

  • Braintrust has a governed set of core entities and metrics that Product, Finance, and GTM all build on

  • Product usage data is reliable enough that people act on it without checking it against something else first

  • Stakeholders can answer their own questions through trusted, self-serve models

  • Every automated decision and every metric has a traceable path back to its source

  • The foundation is documented and maintainable. Simple enough for a startup team to run solo, structured enough to hand to a larger data team as it grows.

Benefits include

  • Medical, dental, and vision insurance

  • Daily lunch, snacks, and beverages

  • Flexible time off

  • Competitive salary and equity

  • Wifi & cellphone stipend

Equal opportunity

Braintrust is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Source details

View original posting ↗

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United States

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