Bullhorn-api Python API Docs | dltHub

Bullhorn API is a RESTful service for talent acquisition and staffing data. The REST API base URL is https://rest-{value_from_loginInfo}.bullhornstaffing.com/rest-services and All requests require a BhRestToken (session token) obtained via OAuth2 and login..

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 Bullhorn-api data in under 10 minutes.

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What data can I load from Bullhorn-api?

Here are some of the endpoints you can load from Bullhorn-api:

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How do I authenticate with the Bullhorn-api API?

First obtain an OAuth2 access token, then POST to the /login endpoint with that token to receive a BhRestToken session token. Include BhRestToken in each request (e.g., as a query parameter).

1. Get your credentials

1. Log in to the Bullhorn developer portal.
2. Register a new application to receive a client ID and client secret.
3. Follow Bullhorn's OAuth2 documentation to exchange the client credentials for an access token (e.g., via the Authorization Code grant).
4. Use that access token in the /login call to obtain a BhRestToken.

2. Add them to .dlt/secrets.toml

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[sources.bullhorn_api_source]
access_token = "your_oauth_access_token_here"
BhRestToken = "your_bh_rest_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 Bullhorn-api 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 bullhorn_api_pipeline.py

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

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Pipeline bullhorn_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bullhorn_api_data
The duckdb destination used duckdb:/bullhorn_api.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 bullhorn_api_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 candidate and job_order from the Bullhorn-api 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 bullhorn_api_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://rest-{value_from_loginInfo}.bullhornstaffing.com/rest-services",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "candidate", "endpoint": {"path": "entity/Candidate/{id}", "data_selector": "data"}},
            {"name": "job_order", "endpoint": {"path": "entity/JobOrder/{id}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bullhorn_api_pipeline",
        destination="duckdb",
        dataset_name="bullhorn_api_data",
    )
    load_info = pipeline.run(bullhorn_api_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("bullhorn_api_pipeline").dataset()
sessions_df = data.candidate.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bullhorn_api_data.candidate LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bullhorn_api_pipeline").dataset()
data.candidate.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 Bullhorn-api 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

• Cause: Invalid or expired OAuth2 access token or BhRestToken.
• Symptoms: API returns HTTP 401 Unauthorized.
• Resolution: Refresh the OAuth2 access token and repeat the login call to obtain a new BhRestToken.

Rate limits

• Cause: Exceeding the allowed number of login calls or API requests per minute.
• Symptoms: HTTP 429 Too Many Requests.
• Resolution: Implement exponential backoff and respect the X-Rate-Limit-* headers if provided.

Session expiry

• Cause: BhRestToken session expires after inactivity.
• Symptoms: Subsequent API calls return 401 with a message about an invalid session.
• Resolution: Re‑login using the OAuth2 access token to obtain a fresh BhRestToken.

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