When I Work Python API Docs | dltHub

When I Work is a workforce management platform that provides scheduling, time tracking, and payroll-related REST APIs for managing users, shifts, times, requests, and related resources. The REST API base URL is https://api.wheniwork.com and Token-based authentication: exchange developer key + user credentials for a login token, then use W-Token or Authorization: Bearer and optionally W-UserId to act on an account.

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 pip install "dlt[workspace]" and start loading When I Work data in under 10 minutes.

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What data can I load from When I Work?

Here are some of the endpoints you can load from When I Work:

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How do I authenticate with the When I Work API?

Authenticate by POSTing to the login service with header 'W-Key: <developer_key>' and JSON body {email, password} to obtain a token. Use the token in subsequent requests either as 'W-Token: ' header or 'Authorization: Bearer ', and optionally include 'W-UserId' to set account context.

1. Get your credentials

1. Obtain a developer key / API key from your When I Work developer dashboard or by contacting When I Work (see Help Center for developer key access).
2. Call the login endpoint: POST https://api.login.wheniwork.com/login with header 'W-Key: <DEVELOPER_KEY>' and JSON body {"email":"<USER_EMAIL>","password":"<USER_PASSWORD>"}.
3. Read the returned person object and retrieve the token (W-Token) from the response; note the issued‑at claim and token lifetime guidance.
4. Use 'W-Token: ' or 'Authorization: Bearer ' plus 'W-UserId: <user_id>' for subsequent API requests.

2. Add them to .dlt/secrets.toml

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[sources.when_i_work_source]
developer_key = "your_developer_key_here"
 token = "your_login_token_here"

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 venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt 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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dlt ai toolkit rest-api-pipeline install

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 When I Work 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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python when_i_work_pipeline.py

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

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Pipeline when_i_work_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset when_i_work_data
The duckdb destination used duckdb:/when_i_work.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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dlt pipeline when_i_work_pipeline 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 users and shifts from the When I Work 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 when_i_work_source(developer_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.wheniwork.com",
            "auth": {
                "type": "bearer",
                "token": developer_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "2/users", "data_selector": "users"}},
            {"name": "shifts", "endpoint": {"path": "2/shifts", "data_selector": "shifts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="when_i_work_pipeline",
        destination="duckdb",
        dataset_name="when_i_work_data",
    )
    load_info = pipeline.run(when_i_work_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("when_i_work_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM when_i_work_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("when_i_work_pipeline").dataset()
data.users.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 When I Work 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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Troubleshooting

Authentication failures

Ensure you exchange your developer key and user credentials at the login endpoint (POST https://api.login.wheniwork.com/login) to get a token. Use that token in the W-Token or Authorization: Bearer <token> header. Tokens typically expire ~7 days; refresh before expiry.

Rate limits and large syncs

The documentation advises using Webhooks for high‑frequency synchronization. Frequent large API requests may hit rate limits (HTTP 429). Prefer webhooks or paginate requests and throttle them to avoid 429 responses.

Pagination and data selectors

Many responses wrap collections in top‑level keys (e.g., users, shifts, times, sites, positions, templates, request-types). Use the exact top‑level key shown in the response sample as the data selector; do not assume a top‑level array. Pagination is handled via page and limit query parameters as described in the endpoint docs.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

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

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

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• 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