Greenhouse (docs)

This package models Greenhouse recruiting data from Fivetran's connector. It uses data in the format described by this ERD.

This package enables you to understand trends in sourcing,recruiting, interviewing, and hiring at your company. It also provides recruiting stakeholders with information about individual applications, interviews, scorecards, and jobs. It achieves all of this by:

• Enriching the core application, interview, and job tables with relevant pipeline data and metrics
• Integrating the interview table with interviewer information and feedback, at both the overall scorecard and individual standard levels
• Calculating the velocity and activity of applications through each pipeline stage, along with major job- and candidate-related attributes for segmented funnel analysis

Models

This package contains transformation models, designed to work simultaneously with our Greenhouse source package. A dependency on the source package is declared in this package's packages.yml file, so it will automatically download when you run dbt deps. The primary outputs of this package are described below. Intermediate models are used to create these output models.

model	description
greenhouse__application_enhanced	Each record represents a unique application, enriched with data regarding the applicant's current stage, source, contact information and resume, associated tags, demographic information, recruiter, coordinator, referrer, hiring managers, and the job they are applying for. Also includes metrics surrounding the candidate's interviews and their volume of activity in Greenhouse.
greenhouse__job_enhanced	Each record represents a unique job, enriched with its associated offices, teams, departments, and hiring team members. Also includes metrics regarding the volume of open, rejected, and hired applications, its active and filled job openings, any job posts, and its active, archived, and converted prospects.
greenhouse__interview_enhanced	Each record represents a unique scheduled interview between an individual interviewer and a candidate (so a panel of 3 interviewers would have 3 records). Includes overall interview feedback, information about the users involved with this interview and application, the application's current status, and data regarding the candidate and the job being interviewed for.
greenhouse__interview_scorecard_detail	Each record represents a unique scorecard attribute, or an individual standard to be rated along for an interview. Includeds information about the candidate, job, and interview at large. Note: this does not include free-form text responses to scorecard questions.
greenhouse__application_history	Each record represents an application advancing to a new stage. Includes data about the time spent in each stage, the volume of activity per stage, the application source, candidate demographics, recruiters, and hiring managers, as well as the job's team, office, and department.

Installation Instructions

Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.

Configuration

By default, this package looks for your Greenhouse data in the greenhouse schema of your target database. If this is not where your Greenhouse data is, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
    greenhouse_database: your_database_name
    greenhouse_schema: your_schema_name 

Passing Through Custom Columns

The Greenhouse APPLICATION, JOB, AND CANDIDATE tables may all have custom columns, all prefixed with custom_field_. To pass these columns along to the staging and final transformation models, add the following variables to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
    greenhouse_application_custom_columns: ['the', 'list', 'of', 'columns'] # these columns will be in the final application_enhanced model
    greenhouse_candidate_custom_columns: ['the', 'list', 'of', 'columns'] # these columns will be in the final application_enhanced model
    greenhouse_job_custom_columns: ['the', 'list', 'of', 'columns'] # these columns will be in the final job_enhanced model

Disabiling Models

Your Greenhouse connector might not sync every table that this package expects. If your syncs exclude certain tables, it is because you either don't use that functionality in Greenhouse or have actively excluded some tables from your syncs.

To disable the corresponding functionality in the package, you must add the relevant variables. By default, all variables are assumed to be true. Add variables for only the tables you would like to disable:

# dbt_project.yml

...
config-version: 2

vars:
    greenhouse_using_prospects: false # Disable if you do not use prospects and/or do not have the PROPECT_POOL and PROSPECT_STAGE tables synced
    greenhouse_using_eeoc: false # Disable if you do not have EEOC data synced and/or do not want to integrate it into the package models
    greenhouse_using_app_history: false # Disable if you do not have APPLICATION_HISTORY synced and/or do not want to run the application_history transform model

Note: this package only integrates the above variables. If you'd like to disable other models, please create an issue specifying which ones.

Contributions

Don't see a model or specific metric you would have liked to be included? Notice any bugs when installing and running the package? If so, we highly encourage and welcome contributions to this package! Please create issues or open PRs against master. Check out this post on the best workflow for contributing to a package.

Database Support

This package has been tested on BigQuery, Snowflake and Redshift. Coming soon -- compatibility with Spark

Resources:

• Provide feedback on our existing dbt packages or what you'd like to see next
• Have questions, feedback, or need help? Book a time during our office hours here or email us at solutions@fivetran.com
• Find all of Fivetran's pre-built dbt packages in our dbt hub
• Learn how to orchestrate dbt transformations with Fivetran here
• Learn more about Fivetran overall in our docs
• Check out Fivetran's blog
• Learn more about dbt in the dbt docs
• Check out Discourse for commonly asked questions and answers
• Join the chat on Slack for live discussions and support
• Find dbt events near you
• Check out the dbt blog for the latest news on dbt's development and best practices