Load Workday data to Snowflake

Workday REST API is a tenant-hosted interface for programmatic access to Workday human capital management data and services. Everything needed to build a working Workday → Snowflake pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Workday to Snowflake pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Workday API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Workday API at a glance

These values come from the Workday API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Workday API?

Workday REST APIs use OAuth 2.0; requests must include an Authorization header with a Bearer access token and a Content-Type: application/json header.

1. Get your credentials

1. Log into your Workday instance and search for the 'Create Integration System User' (ISU) task to create a dedicated user for your API integration. Ensure the ISU is exempt from password expiration and session timeouts.\n2. Create an Integration System Security Group and assign the ISU to it. Grant this security group the necessary domain security policies for the REST API resources you intend to access.\n3. Search for 'Register API Client for Integrations' in Workday. \n4. In the registration form, set the Client Name, choose 'Client Credentials' as the Grant Type (preferred for server-to-server pipelines), and select the required API scopes (e.g., 'Integration', 'System').\n5. Save the registration to generate your 'Client ID' and 'Client Secret'. Note that the Client Secret is displayed only once; store it securely in a secrets manager immediately.\n6. Navigate to the 'Manage Integration System Users' task to link the newly created API Client to your ISU to define the security context for API requests.\n7. Retrieve the required endpoint URLs (REST API Endpoint, Token Endpoint, Authorization Endpoint) from the API Client details page in Workday.

2. Add them to .dlt/secrets.toml

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[sources.workday_source]
access_token = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Workday data can I load into Snowflake?

These are the Workday endpoints dlt can load into Snowflake:

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How do I load only new Workday records?

The Workday API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "workers", "endpoint": {
    "path": "workers",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Workday pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading token and authorize from the Workday API into Snowflake:

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

@dlt.source
def workday_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{TENANT}.workday.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "workers", "endpoint": {"path": "workers", "data_selector": "data"}},
            {"name": "custom_reports", "endpoint": {"path": "{reportOwner}/{reportName}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_workday_to_snowflake() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="workday_pipeline",
        destination="snowflake",
        dataset_name="workday_data",
    )
    load_info = pipeline.run(workday_source())
    print(load_info)


if __name__ == "__main__":
    load_workday_to_snowflake()

Run it with uv run python workday_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Workday data in Snowflake?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

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

SQL:

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SELECT * FROM workday_data.workers LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Workday to Snowflake pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Workday loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Workday data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack