Hailey HR Python API Docs | dltHub

Hailey HR is an HRIS platform that provides a REST API to access people data for integration and automation purposes. The REST API base URL is https://api.haileyhr.app and requests require a personal access token (API key) passed in the Authorization 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 Hailey HR data in under 10 minutes.

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What data can I load from Hailey HR?

Here are some of the endpoints you can load from Hailey HR:

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How do I authenticate with the Hailey HR API?

Authentication is performed using a personal access token (often referred to as an API key) generated via the Hailey dashboard. Requests require the token to be passed in the Authorization header, typically formatted as 'Authorization: Bearer '.

1. Get your credentials

To obtain an API key for the Hailey HR REST API, follow these steps: \n1. Log in to your Hailey HR account dashboard.\n2. Click on your profile avatar or initials (located in the corner of the screen).\n3. Select 'Access tokens' from the menu.\n4. In the 'New access token' section, provide a name for the token.\n5. Click 'Generate'.\n6. Copy the generated token immediately, as it will only be displayed once. Ensure you have the necessary administrative or integration-edit permissions to create these tokens. Note that these tokens are typically valid for one year and will need to be regenerated periodically.

2. Add them to .dlt/secrets.toml

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[sources.hailey_hr_source]
api_key = "your_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 Hailey HR 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 hailey_hr_pipeline.py

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

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Pipeline hailey_hr_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hailey_hr_data
The duckdb destination used duckdb:/hailey_hr.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 hailey_hr_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 employees and employments from the Hailey HR 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 hailey_hr_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.haileyhr.app",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "employees", "endpoint": {"path": "Employees"}},
            {"name": "employments", "endpoint": {"path": "Employments"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hailey_hr_pipeline",
        destination="duckdb",
        dataset_name="hailey_hr_data",
    )
    load_info = pipeline.run(hailey_hr_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("hailey_hr_pipeline").dataset()
sessions_df = data.employees.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM hailey_hr_data.employees LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("hailey_hr_pipeline").dataset()
data.employees.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 Hailey HR 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