Features
data-engineer.ai is easy to use and framework agnostic
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about items including name and category
Dimensional Modeling
Create declarative pipelines using dimension and fact tables
Data Warehouses
Integrates with Snowflake, Databricks, Postgres and more
dbt and SQLMesh
Once you're ready to productionize, export them into dbt or SQLMesh
Text-to-SQL
We incorporate best practices to create production grade SQL queries
Orchestration (coming soon...)
Create CRON schedules to automate your jobs
How it works
Get started in 5 simple steps
Setup a connection
Follow the instructions to connect your data warehouse: choose between Snowflake, Databricks or Postgres.
Write a prompt
Write a prompt or pick a Template. You should see "Tables" which shows your source tables.
Iterate
Continue iterating on your models, through prompting or manual adjustments as needed.
Export
Once you're ready to productionize your code, click Export. Choose between dbt or SQLMesh.
Download
Continue working on this yourself, or hand it over to your Engineer!
FAQs
data-engineer.ai is a tool that allows you to prototype dbt or SQLMesh models quickly. We help Data Analyst and Data Scientists transform their raw data into insights using:
- Dimensional Modeling: create fact/dim tables on the fly.
- Context Engineering: we'll handle it for you, so you can focus on the business logic.
- Modern UX/UI: simple and intuitive design.
- Avoid Vendor Lock-in: Export your project into dbt or SQL Mesh
Most AI data tools get you 70% of the way there. The other 30% is where most people spend time iterating. We built data-engineer.ai with these features to help you get 100% of the way there.