Bamboohr Python API Docs | dltHub

BambooHR is a cloud-based HR platform that provides a REST API for accessing and managing employee, company, time‑off, payroll-related and other HR data. The REST API base URL is https://{companyDomain}.bamboohr.com/api/v1 and Requests use either OAuth2 (Bearer tokens) or HTTP Basic authentication with a per‑user API key..

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 Bamboohr data in under 10 minutes.

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What data can I load from Bamboohr?

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

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How do I authenticate with the Bamboohr API?

For OAuth2: obtain client_id and client_secret, perform the authorization flow at https://{companyDomain}.bamboohr.com/authorize.php and exchange the code at /token.php to receive a Bearer token. For API‑key: create an API key in the user menu and send it as the username in HTTP Basic auth (any password).

1. Get your credentials

1. Sign in to BambooHR and open the Developer Portal (https://developers.bamboohr.com). 2) Create a new application to get a client_id and client_secret (for OAuth). 3) Register a Redirect URI. 4) For a per‑user API key: in BambooHR UI click your name → API Keys → Create New Key; copy the generated key.

2. Add them to .dlt/secrets.toml

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[sources.bamboohr_source]
api_key = "your_api_key_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 Bamboohr 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 bamboohr_pipeline.py

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

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Pipeline bamboohr_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bamboohr_data
The duckdb destination used duckdb:/bamboohr.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 bamboohr_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 employees/directory from the Bamboohr 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 bamboohr_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{companyDomain}.bamboohr.com/api/v1",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "employees", "endpoint": {"path": "employees", "data_selector": "employees"}},
            {"name": "employee_directory", "endpoint": {"path": "employees/directory", "data_selector": "employees"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM bamboohr_data.employees LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bamboohr_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 Bamboohr 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

If you receive 401 Unauthorized, verify you are using the correct authentication method. For API‑key Basic auth, supply the API key as the username and any string as the password. For OAuth, include the Bearer token in the Authorization header. Repeated invalid API‑key attempts may result in 403 Forbidden and temporary disabling.

Rate limiting and 503 / Retry

Requests may be throttled; endpoints can return 503 Service Unavailable for heavy traffic. Respect the Retry‑After header when present and implement exponential backoff.

Error headers and debugging

Many 4xx and some 5xx responses include an X‑BambooHR-Error-Message header with additional diagnostic text. Log this header for debugging but do not rely on its exact content for program logic.

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