Founding Analytics Engineer — Self-Serve Data Platform

Braintrust; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Medical insurance

Daily meals

Flexible time off

Equity

Job summary

Braintrust is seeking an analytics engineer to define the data foundation, model usage data, and own ingestion pipelines that feed trusted metrics.

You will partner with executives and cross‑functional teams—Product, Finance, GTM—to shape event schemas and semantic models, enabling self‑serve analytics across the business.

This founding, hands‑on role combines data modeling, architecture decisions, and governance to keep analytics reliable as the company scales.

Qualifications

• Defined business entities and metrics used across Product, Finance and GTM.
• Own ingestion pipelines from product events, CRM, and other systems.
• Architect data models and ensure trusted, self-serve analytics.

Responsibilities

• Define business entities, metrics, and semantics used across teams.
• Design and build core data models in SQL and dbt.
• Own ingestion from product events, CRM, and other systems.
• Make architectural decisions for storage, orchestration, and transformation.
• Stand up self-serve analytics with semantic models and trusted metrics.
• Partner with Engineering and Product on usage data and event schemas.
• Ensure governance and reliability as part of the data model.
• Use AI to accelerate development while keeping foundations clean.

Skills

SQL

dbt

Snowflake

Dagster

Airflow

Data modeling

Governance

Production code

Tools

dbt

Dagster

Airflow

Fivetran

Terraform

GitHub Actions

Soda

Omni

Looker

Job description

Braintrust is seeking an analytics engineer to define the data foundation, model usage data, and own ingestion pipelines that feed trusted metrics.

You will partner with executives and cross‑functional teams—Product, Finance, GTM—to shape event schemas and semantic models, enabling self‑serve analytics across the business.

This founding, hands‑on role combines data modeling, architecture decisions, and governance to keep analytics reliable as the company scales.