Workday HCM dbt Package

This dbt package transforms data from Fivetran's Workday HCM connector into analytics-ready tables.

Resources

• Number of materialized models¹: 29
• Connector documentation
  • Workday connector documentation
  • Workday ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog

What does this dbt package do?

This package enables you to transform core object tables into analytics-ready models and gather daily historical records of employees. It creates enriched models with metrics focused on employee demographics, organizational structures, job profiles, and position management.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_workday

Final output tables

By default, this package materializes the following final tables:

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 Workday HCM connection syncing data into your destination.
• A BigQuery, Snowflake, Redshift, Databricks, or PostgreSQL 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.

Install the package

Include the following Workday HCM 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/workday
    version: [">=0.8.0", "<0.9.0"] # we recommend using ranges to capture non-breaking changes automatically

Databricks dispatch configuration

If you are using a Databricks destination with this package, you must add the following (or a variation of the following) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Define database and schema variables

Single connection

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

# dbt_project.yml

vars:
    workday_database: your_database_name
    workday_schema: your_schema_name

Union multiple connections

If you have multiple Workday HCM connections 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 workday_union_schemas OR workday_union_databases variables (cannot do both) in your root dbt_project.yml file:

# dbt_project.yml

vars:
    workday_union_schemas: ['workday_usa','workday_canada'] # use this if the data is in different schemas/datasets of the same database/project
    workday_union_databases: ['workday_usa','workday_canada'] # use this if the data is in different databases/projects but uses the same schema name

NOTE: 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) Utilizing Workday HCM History Mode

If you have History Mode enabled for your Workday HCM connection, we now include support for the worker, worker position, worker position organization, and personal information tables directly. You can view these files in the staging folder. This staging data then flows into the employee daily history model, which in turn populates the monthly summary model. This will allow you access to your historical data for these tables for the most accurate record of your data over time.

Enabling Workday HCM History Mode Models

The History Mode models can get quite expansive since it will take in ALL historical records, so we've disabled them by default. You can enable the history models you'd like to utilize by adding the below variable configurations within your dbt_project.yml file for the equivalent models.

# dbt_project.yml

...
vars:
  employee_history_enabled: true  # False by default. Only use if you have history mode enabled and wish to view the full historical record. 

Filter your Workday HCM History Mode models

By default, these history models are set to bring in all your data from Workday HCM History, but you may be interested in bringing in only a smaller sample of historical records, given the relative size of the Workday HCM history source tables. By default, the package will use the minimum _fivetran_start date for the historical end models. This default may be overwritten to your liking by leveraging the below variable.

We have set up where conditions in our staging models to allow you to bring in only the data you need to run in. You can set a global history filter that would apply to all of our staging history models in your dbt_project.yml:

vars:
    employee_history_start_date: 'YYYY-MM-DD' # The first `_fivetran_start` date you'd like to filter data on in all your history models.

The default date value in our models is set at 2005-03-01 (the month Workday was founded), designed for if you want to capture all available data by default. If you choose to set a custom date value as outlined above, these models will take the greater of either this value or the minimum _fivetran_start date in the source data. They will then be used for creating the first dates available with historical data in your daily history models.

(Optional) Additional configurations

Changing the Build Schema

By default this package will build the Workday HCM staging models within a schema titled (<target_schema> + _stg_workday) and the Workday HCM final models within a schema titled (<target_schema> + _workday) in your target database. If this is not where you would like your modeled Workday HCM data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

models:
  workday:
    +schema: my_new_schema_name # leave blank for just the target_schema
    staging:
        +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.

# dbt_project.yml

vars:
    workday_<default_source_table_name>_identifier: your_table_name 

(Optional): Workday Schema Migration Configuration

Workday is migrating to a new API version with significant schema changes that will last for several months. Starting January 5, 2026, existing Fivetran Workday HCM connectors will begin syncing new tables with an "_INCOMING" suffix alongside existing tables during a transition period lasting until April 6, 2026. This package automatically detects which tables are available in your warehouse and uses the appropriate tables. No action is required in most cases.

Impacted Tables

The following tables have new versions with "_incoming" suffix:

• military_service → military_service_incoming
• personal_information_ethnicity → personal_information_ethnicity_incoming

Additionally, fields from personal_information_history have been split into new tables:

• personal_information_common_data
• country_personal_information

Leveraging Legacy or Incoming Table Names

If you need to leverage the old personal information schema or have set up a Workday HCM connector after January 5, you can set the following variables in your dbt_project.yml:

# dbt_project.yml

vars: 
  workday__using_military_service_incoming: false  # Default is currently true
  workday__using_personal_information_ethnicity_incoming: false  # Default is currently true 
  workday__using_personal_info_v2_schema: false  # To leverage old schema. Default is currently true

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

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/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. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.

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.