Workday HCM Python API Docs | dltHub

Workday HCM REST API provides access to human capital management data and services through a configurable, role-based security model managed via API gateways or tenant-specific endpoints. The REST API base URL is The base URL is region-dependent, typically following the format 'https://api.{region}.wcp.workday.com' for Workday Extend/Orchestration or 'https://{tenantHostname}/api/{serviceName}/{version}/{tenant}' for legacy integrations. and All requests require a Bearer token generated via an OAuth 2.0 flow..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading Workday HCM data in under 10 minutes.

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What data can I load from Workday HCM?

Here are some of the endpoints you can load from Workday HCM:

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How do I authenticate with the Workday HCM API?

Workday REST APIs require an OAuth 2.0 access token passed in the Authorization header as 'Bearer {access_token}'.

1. Get your credentials

To obtain credentials for the Workday HCM REST API, you must register an OAuth API client within your Workday tenant. Follow these steps: 1. Sign in to Workday as an Administrator. 2. Search for the task 'Register API Client for Integrations'. 3. Enter a Client Name, select the appropriate Scopes (functional areas), and decide on refresh token settings (e.g., set 'Non-Expiring Refresh Tokens' based on security policy). 4. After clicking OK, copy the generated 'Client ID' and 'Client Secret'. 5. To generate a refresh token, search for the 'View API Client' task, select your new client, and navigate to the Related Actions menu > API Client > Manage Refresh Tokens for Integrations. 6. Select the integration user, generate the new refresh token, and record it. Ensure the tenant security policy is configured to allow OAuth 2.0 clients (via 'Edit Tenant Setup - Security').

2. Add them to .dlt/secrets.toml

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[sources.workday_hcm_source]
client_id = "REPLACE_ME"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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uv run dlthub ai toolkit install rest-api-pipeline

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Workday HCM API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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uv run python workday_hcm_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline workday_hcm_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset workday_hcm_data
The duckdb destination used duckdb:/workday_hcm.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads /workers and /staffing from the Workday HCM API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def workday_hcm_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is region-dependent, typically following the format 'https://api.{region}.wcp.workday.com' for Workday Extend/Orchestration or 'https://{tenantHostname}/api/{serviceName}/{version}/{tenant}' for legacy integrations.",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "workers", "endpoint": {"path": "workers", "data_selector": "data"}},
            {"name": "reports", "endpoint": {"path": "{reportOwner}/{reportName}", "data_selector": "Report_Entry"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="workday_hcm_pipeline",
        destination="duckdb",
        dataset_name="workday_hcm_data",
    )
    load_info = pipeline.run(workday_hcm_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("workday_hcm_pipeline").dataset()
sessions_df = data.workers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM workday_hcm_data.workers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("workday_hcm_pipeline").dataset()
data.workers.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Workday HCM data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack