Lever Transformation dbt Package (Docs)

What does this dbt package do?

• Produces modeled tables that leverage Lever data from Fivetran's connector in the format described by this ERD and builds off the output of our Lever source package.

NOTE: If your Lever connector was created prior to July 2020 or still uses the Candidate endpoint, you must fully re-sync your connector or set up a new connector to use Fivetran's Lever dbt packages.

• Enables you to understand trends in recruiting, interviewing, and hiring at your company. It also provides recruiting stakeholders with information about individual opportunities, interviews, and jobs. It achieves this by:
  • Enriching the core opportunity, interview, job posting, and requisition tables with relevant pipeline data and metrics
  • Integrating the interview table with reviewer information and feedback
  • Calculating the velocity of opportunities through each pipeline stage, along with major job- and candidate-related attributes for segmented funnel analysis
• Generates a comprehensive data dictionary of your source and modeled Lever data through the dbt docs site.

The following table provides a detailed list of all tables materialized within this package by default.

TIP: See more details about these tables in the package's dbt docs site.

Table	Description
lever__interview_enhanced	Each record represents a score that an interviewer gives to a unique interviewee. Includes data around the employees involved in this interview/opportunity, the interview feedback score standards, whether the opportunity advanced past this interview, how long the opportunity had been open at the time of the interview, and the opportunity source.
lever__opportunity_enhanced	Each record represents a unique opportunity, enhanced with data about its associated job posting, requisition, application, origins, tags, resume links, contact information, current pipeline stage, offer status, and the position that the candidate applied for. Also includes interview metrics and how early the candidate applied relative to other candidates.
lever__posting_enhanced	Each record represents a unique job posting, enriched with metrics about submitted applications, total and open opportunities, interviews conducted, and associated requisitions. Also includes the job posting's tags and hiring manager.
lever__requisition_enhanced	Each record represents a unique job requisition, enriched with information about the requisition's hiring manager, owner, offers extended, and associated job postings.
lever__opportunity_stage_history	Each record represents a stage that an opportunity has advanced to. Includes data about the time spent in each stage, the application source, the hiring manager, and the opportunity's owner, as well as the job's team, location, and department.

How do I use the dbt package?

Step 1: Prerequisites

To use this dbt package, you must have the following:

• At least one Fivetran Lever connector syncing data into your destination.
• A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Step 2: Install the package

Include the following lever package version in your packages.yml file:

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

packages:
  - package: fivetran/lever
    version: [">=0.7.0", "<0.8.0"]

Do NOT include the lever_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.

Step 3: Define database and schema variables

By default, this package runs using your destination and the lever schema. If this is not where your Lever data is (for example, if your Lever schema is named lever_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    lever_database: your_database_name
    lever_schema: your_schema_name 

Step 4: Disable models for non-existent sources

Your Lever 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 Lever or have actively excluded some tables from your syncs. To disable the corresponding functionality in the package, you must set the relevant config variables to false. By default, all variables are set to true. Alter variables for only the tables you want to disable:

# dbt_project.yml
...
config-version: 2

vars:
    lever_using_requisitions: false # Disable if you do not have the requisition table, or if you do not want requisition related metrics reported
    lever_using_posting_tag: false # disable if you do not have (or want) the postings tag table

(Optional) Step 5: Additional configurations

Expand/collapse configurations

Passing Through Custom Requisition Columns

If you choose to include requisitions, the REQUISITION table may also have custom columns (all prefixed by custom_field_). To pass these columns through to the enhanced requisition model, add the following variable to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
    lever_requisition_passthrough_columns: ['the', 'list', 'of', 'fields']

Change the build schema

By default, this package builds the Lever staging models within a schema titled (<target_schema> + _stg_lever) and your Lever modeling models within a schema titled (<target_schema> + _lever) in your destination. If this is not where you would like your Lever data to be written to, add the following configuration to your root dbt_project.yml file:

models:
    lever_source:
      +schema: my_new_schema_name # leave blank for just the target_schema
    lever:
      +schema: my_new_schema_name # leave blank for just the target_schema

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

vars:
    lever_<default_source_table_name>_identifier: your_table_name 

Leveraging Legacy Connector Table Names

For Fivetran Lever connectors created on or after July 27, 2024, the USER and INTERVIEWER_USER source tables have been renamed to USERS and INTERVIEW_USER, respectively. This package now prioritizes the USERS and INTERVIEW_USER tables if available, falling back to USER and INTERVIEWER_USER if not.

If you have both tables in your schema and would like to specify this package to leverage the USER and/or INTERVIEWER_USER tables, you can set the variables lever__using_users and/or lever__using_interview_user to false in your dbt_project.yml.

vars:
    lever__using_users: false # Default is true to use USERS. Set to false to use USER.
    lever__using_interview_user: false # Default is true to use INTERVIEW_USER. Set to false to use INTERVIEWER_USER.

Union multiple connectors

If you have multiple lever connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either the lever_union_schemas OR lever_union_databases variables (cannot do both) in your root dbt_project.yml file:

vars:
    lever_union_schemas: ['lever_usa','lever_canada'] # use this if the data is in different schemas/datasets of the same database/project
    lever_union_databases: ['lever_usa','lever_canada'] # use this if the data is in different databases/projects but uses the same schema name

Please be aware that the native source.yml connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined source.yml.

To connect your multiple schema/database sources to the package models, follow the steps outlined in the Union Data Defined Sources Configuration section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core setup guides.

Does this package have dependencies?

This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/lever_source
      version: [">=0.7.0", "<0.8.0"]

    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

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. Check out this dbt Discourse article on the best workflow for contributing to a package.

Are there any resources available?

• If you have questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
• If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.