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
JOBtables with relevant pipeline data and metrics
- Integrating the
INTERVIEWtable 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
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.
|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. 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. 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 three interviewers will have three 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. Includes information about the candidate, job, and interview at large. Note: 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.|
Include in your
packages: - package: fivetran/greenhouse version: [">=0.4.0", "<0.5.0"]
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
... config-version: 2 vars: greenhouse_database: your_database_name greenhouse_schema: your_schema_name
Passthrough Custom Columnslink
CANDIDATE tables may 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
... 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
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:
... 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 greenhouse_using_job_office: false # Disable if you do not have JOB_OFFICE and/or OFFICE synced, or do not want to include offices in the job_enhanced transform model greenhouse_using_job_department: false # Disable if you do not have JOB_DEPARTMENT and/or DEPARTMENT synced, or do not want to include offices in the job_enhanced 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.
Changing the Build Schemalink
By default this package will build the Greenhouse Source staging models within a schema titled (<target_schema> +
_stg_greenhouse) and the Greenhouse final transform models within a schema titled (<target_schema> +
_greenhouse) in your target database. If this is not where you would like you Greenhouse staging and final models to be written to, add the following configuration to your
... models: greenhouse: +schema: my_new_final_models_schema # leave blank for just the target_schema greenhouse_source: +schema: my_new_staging_models_schema # leave blank for just the target_schema
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
main. See the Discourse post for information on how to contribute to a package.
This package has been tested on BigQuery, Snowflake and Redshift.
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