Load Breezy HR data to DuckDB

Breezy HR is a recruiting and applicant tracking platform that provides a REST API for managing candidates, positions, pipelines, and recruitment activity data. Everything needed to build a working Breezy HR → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

---

Build your Breezy HR to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Breezy HR to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Breezy HR API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

---

Breezy HR API at a glance

These values come from the Breezy HR API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Breezy HR API?

All requests (except /v3/signin and /v3/health) require an 'Authorization' header containing the Personal Access Token (PAT) or session access token. Personal Access Tokens are prepended with 'breezypat'.

1. Get your credentials

To obtain an API key (Personal Access Token) for Breezy HR, follow these steps: 1. Sign in to your Breezy HR account. 2. Click your profile/user icon located in the bottom-left corner of the sidebar. 3. Navigate to 'My Settings'. 4. Click on the 'API Keys' tab near the top of the screen. 5. Click the '+ Create API Key' button. 6. Enter a descriptive name for the key and select an expiration timeframe. 7. Click 'Create Key'. The API key will be displayed; copy it immediately as it is only shown once and cannot be retrieved later. PATs carry the prefix 'breezypat'.

2. Add them to .dlt/secrets.toml

Copy
[sources.breezy_hr_source]
api_key = "breezypat_your_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

---

What Breezy HR data can I load into DuckDB?

These are the Breezy HR endpoints dlt can load into DuckDB:

---

How do I load only new Breezy HR records?

Breezy HR exposes updated on v3/company/{company_id}/positions, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

Copy
{"name": "company_positions", "endpoint": {
    "path": "v3/company/{company_id}/positions",
    "incremental": {"cursor_path": "updated", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

---

What does the generated Breezy HR pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v3/companies and /v3/company/{company_id}/webhook_endpoints from the Breezy HR API into DuckDB:

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": "api_key", "api_key": api_key, "name": "Authorization"},
        },
        "resources": [
            {"name": "company_positions", "endpoint": {"path": "v3/company/{company_id}/positions"}},
            {"name": "position_candidates", "endpoint": {"path": "v3/company/{company_id}/position/{position_id}/candidates"}}
        ],
    }
    yield from rest_api_resources(config)


def load_breezy_hr_to_duckdb() -> 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)


if __name__ == "__main__":
    load_breezy_hr_to_duckdb()

Run it with python breezy_hr_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I query Breezy HR data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

Copy
import dlt

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

SQL:

Copy
SELECT * FROM breezy_hr_data.company_positions LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

---

How do I deploy the Breezy HR to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Breezy HR loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Breezy HR data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

---

Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack