Lever dbt Package
This dbt package transforms data from Fivetran's Lever connector into analytics-ready tables.
Resources
- Number of materialized models¹: 53
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to understand trends in recruiting, interviewing, and hiring at your company. It creates enriched models with metrics focused on opportunities, interviews, job postings, and requisitions.
NOTE: If your Lever connection was created prior to July 2020 or still uses the Candidate endpoint, you must fully re-sync your connection or set up a new connection to use Fivetran's Lever dbt packages.
Output schema
Final output tables are generated in the following target schema:
<your_database>.<connector/schema_name>_lever
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| lever__interview_enhanced | Tracks individual interview feedback scores with data on interviewers, candidates, and opportunity progression to evaluate interview quality and hiring decisions. Example Analytics Questions:
|
| lever__opportunity_enhanced | Provides comprehensive candidate opportunity data including pipeline stage, offer status, job posting details, interview metrics, and application timing to manage the hiring process end-to-end. Example Analytics Questions:
|
| lever__posting_enhanced | Summarizes job posting performance with metrics on applications, opportunities, interviews, and requisitions to understand hiring demand and posting effectiveness. Example Analytics Questions:
|
| lever__requisition_enhanced | Tracks job requisitions with hiring manager information, offers extended, and associated postings to monitor headcount planning and hiring progress. Example Analytics Questions:
|
| lever__opportunity_stage_history | Chronicles opportunity progression through hiring stages with time-in-stage metrics, source attribution, and team assignments to identify pipeline bottlenecks and hiring velocity. 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 Lever 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.