Data Engineer

Braintrust•New York, NY

16h

About The Position

Braintrust is the AI observability platform. By connecting evals and observability in one workflow, Braintrust gives builders the visibility to understand how AI behaves in production and the tools to improve it. Teams at Notion, Stripe, and Vercel use Braintrust to compare models, test prompts, and catch regressions — turning production data into better AI with every release. Braintrust is growing quickly across enterprise and self-serve customers, and our internal systems need to scale with us. We need someone who can come in, understand the business, and create the data systems that help us operate with clarity. This is a hands-on role and, for now, a solo one. You will not be joining a large data team with mature processes already in place. You will be one of the first people building the data layer for our business operations and systems function, which means you should be comfortable moving between architecture, pipelines, data modeling, metrics definitions, dashboards, and day-to-day business questions. The right person has strong technical depth, understands how GTM teams work, and is excited to build with AI. You should have strong opinions about where agents can automate operational workflows, and the judgment to build the data systems, context, and feedback loops that make those workflows work. You will partner with Product and Engineering on usage data and event schemas, while working closely with Sales, RevOps, Marketing, and Finance on account health, pipeline, retention, expansion, forecasting, and revenue reporting.

Requirements

10+ years in data engineering, analytics engineering, data architecture, or business systems data roles.
Excellent communication skills and ability to work across technical and non-technical teams.
Strong experience building data systems in developer tools, infrastructure, AI, or another technical environment.
Deep experience with SQL, data modeling, pipelines, orchestration, transformation, and production-grade code.
Experience working across product telemetry, CRM, billing, customer health, marketing, support, finance, or other operational systems.
Strong understanding of how GTM teams operate, especially Sales, RevOps, Marketing, and Finance.
Deep curiosity about AI and a strong point of view on how it will change data engineering, analytics, and business operations.
Hands-on experience using AI tools in your own work to write code, analyze data, automate workflows, improve documentation, or accelerate operational tasks.
Comfortable designing systems where humans and agents work together, with the right data, context, guardrails, and feedback loops.
Comfortable operating as the only data hire in a startup environment, balancing foundational architecture with urgent business needs.

Nice To Haves

Braintrust user :)
You have worked at a high-growth startup where the data function was still being built.
You have experience with enterprise and self-serve revenue motions.
You have worked with usage-based pricing, consumption models, product-led growth, or sales-assisted funnels.
You have built customer health, activation, retention, expansion, forecasting, or revenue intelligence systems.
You have supported enterprise sales motions, including account scoring, pipeline analytics, territory planning, renewal workflows, or expansion reporting.
You have built or used agents for internal operations, data QA, customer research, enrichment, reporting, or workflow automation.
You have experience in AI, developer tools, infrastructure, observability, or data platform companies.

Responsibilities

Build and own the core data models that connect product usage, accounts, customers, revenue, pipeline, billing, and customer health.
Create trusted sources of truth for the metrics Braintrust uses to run the business, including activation, usage, retention, expansion, pipeline, ARR, and usage-based revenue.
Build pipelines across product telemetry, CRM, billing, customer success, marketing, support, finance, and AI-powered internal systems.
Partner with Engineering and Product to improve the quality, consistency, and usability of product data.
Partner with Sales, RevOps, Marketing, and Finance to turn messy operational questions into durable data models and workflows.
Build dashboards, datasets, and self-serve reporting that help teams answer common questions without relying on one-off analysis.
Use AI to speed up your own work and identify where agents can reduce manual reporting, enrichment, QA, routing, research, and operational follow-up.
Help design the data layer for future AI-native business operations workflows, including clean context, structured inputs, feedback loops, and evaluation of outputs.
Improve data quality through testing, monitoring, documentation, lineage, and clear ownership.
Support business planning and operating cadence by making sure leadership has accurate visibility into customer usage, GTM performance, and revenue health.
Make pragmatic tradeoffs. Some work will be foundational architecture, some will be unblocking urgent business questions, and the job is knowing how to balance both.