Deel Python API Docs | dltHub

Deel is an HR and global payroll platform that provides a REST API for managing contracts, workers, invoices, and payments. The REST API base URL is https://api.letsdeel.com/rest and all requests require a Bearer token in the Authorization header; OAuth2 flows also require an x-client-id header.

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 Deel data in under 10 minutes.

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What data can I load from Deel?

Here are some of the endpoints you can load from Deel:

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How do I authenticate with the Deel API?

The Deel API uses bearer token authentication. Requests require an 'Authorization' header in the format 'Bearer '. Additionally, when using OAuth2, an 'x-client-id' header is also required.

1. Get your credentials

1. Log in to your Deel dashboard at app.deel.com. 2. Select the 'More' icon and navigate to 'Developer Center' (or navigate directly via Settings > Developer). 3. Click the 'Access Tokens' or 'API access' tab. 4. Click 'Generate new token'. 5. Select the token type (Personal or Organization). 6. Configure the required scopes for your integration. 7. Click 'Generate' and copy the token immediately, as it cannot be retrieved again.

2. Add them to .dlt/secrets.toml

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[sources.deel_source]
api_key = "your_deel_access_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 harness:

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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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dlthub 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 Deel 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 deel_pipeline.py

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

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Pipeline deel_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset deel_data
The duckdb destination used duckdb:/deel.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 deel_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 /contracts and /people from the Deel 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 deel_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.letsdeel.com/rest",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "people", "endpoint": {"path": "rest/people", "data_selector": "data"}},
            {"name": "contracts", "endpoint": {"path": "rest/v2/contracts", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="deel_pipeline",
        destination="duckdb",
        dataset_name="deel_data",
    )
    load_info = pipeline.run(deel_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("deel_pipeline").dataset()
sessions_df = data.people.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM deel_data.people LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("deel_pipeline").dataset()
data.people.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 Deel 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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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform 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