Greenhouse dbt Package
This dbt package transforms data from Fivetran's Greenhouse connector into analytics-ready tables.
Resources
- Number of materialized models¹: 68
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to understand trends in sourcing, recruiting, interviewing, and hiring at your company. It creates enriched models with metrics focused on applications, interviews, and jobs.
Output schema
Final output tables are generated in the following target schema:
<your_database>.<connector/schema_name>_greenhouse
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| greenhouse__application_enhanced | Tracks all candidate applications with complete applicant profiles including current pipeline stage, recruiter and coordinator assignments, contact information, resume links, and interview activity to manage the hiring funnel. Example Analytics Questions:
|
| greenhouse__job_enhanced | Provides comprehensive job posting data with metrics on application volumes, hiring outcomes, and team assignments to understand job performance and hiring effectiveness. Example Analytics Questions:
|
| greenhouse__interview_enhanced | Tracks individual interviews between interviewers and candidates with feedback scores, interviewer information, and application status to evaluate interview effectiveness and candidate progression. Example Analytics Questions:
|
| greenhouse__interview_scorecard_detail | Captures detailed interview scorecard ratings for each evaluation criterion to analyze interviewer feedback patterns and candidate assessment consistency. Note: Does not include free-form text responses. Example Analytics Questions:
|
| greenhouse__application_history | Chronicles application progression through hiring stages with time-in-stage metrics, activity volumes, and recruiter assignments to analyze hiring velocity and pipeline bottlenecks. Example Analytics Questions:
|
¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.
Prerequisites
To use this dbt package, you must have the following:
- At least one Fivetran Greenhouse connection syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
How do I use the dbt package?
You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:
- To add the package in the Fivetran dashboard, follow our Quickstart guide.
- To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.
How is this package maintained and can I contribute?
Package Maintenance
The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.
Contributions
A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.
We highly encourage and welcome contributions to this package. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.