Breezy HR Python API Docs | dltHub

Breezy HR is an applicant tracking and recruiting platform offering a REST API to manage companies, positions, candidates, pipelines, webhooks and related HR resources. The REST API base URL is https://api.breezy.hr/v3 and All requests require a Bearer access token..

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 Breezy HR data in under 10 minutes.

---

What data can I load from Breezy HR?

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

---

How do I authenticate with the Breezy HR API?

Authenticate by POSTing credentials to https://api.breezy.hr/v3/signin to receive an access_token; include it on subsequent requests as Authorization: Bearer {access_token}.

1. Get your credentials

1. Sign in to the Breezy HR web app as an account admin.
2. Open the Developer/API docs (https://developer.breezy.hr).
3. Use the /signin endpoint with your account credentials to obtain an access_token for API use.
4. Store the returned access_token securely and renew it when it expires.

2. Add them to .dlt/secrets.toml

Copy
[sources.breezy_hr_source]
api_key = "your_breezy_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Breezy 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:

Copy
python breezy_hr_pipeline.py

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

Copy
Pipeline breezy_hr_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset breezy_hr_data
The duckdb destination used duckdb:/breezy_hr.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline breezy_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.

---

Python pipeline example

This example loads positions and candidates from the Breezy HR API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def breezy_hr_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.breezy.hr/v3",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "positions", "endpoint": {"path": "positions", "data_selector": "data"}},
            {"name": "candidates", "endpoint": {"path": "candidates", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="breezy_hr_pipeline",
        destination="duckdb",
        dataset_name="breezy_hr_data",
    )
    load_info = pipeline.run(breezy_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.

---

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):

Copy
import dlt

data = dlt.pipeline("breezy_hr_pipeline").dataset()
sessions_df = data.positions.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM breezy_hr_data.positions LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("breezy_hr_pipeline").dataset()
data.positions.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Breezy 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.

---

Troubleshooting

Authentication failures

If the API returns 401/403: verify you obtained a valid access_token via POST /signin and include header Authorization: Bearer {access_token}. Tokens expire — re‑authenticate and retry.

Rate limiting and throttling

Breezy documents rate limiting; if you receive 429 responses, implement exponential backoff and respect Retry-After headers.

Pagination

List endpoints return paginated results. Use query parameters such as page/per_page or cursor parameters as described in the docs and iterate until no more pages.

Common error responses

• 400 Bad Request: invalid parameters
• 401 Unauthorized: missing/invalid token
• 403 Forbidden: insufficient permissions
• 404 Not Found: invalid resource id
• 429 Too Many Requests: rate limit exceeded
• 500/502/503 Server Errors: retry with backoff

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

---

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.

Copy
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