Load JobNimbus data to DuckDB

JobNimbus is a CRM and project management platform for contractors that provides tools for managing jobs, contacts, and business operations. Everything needed to build a working JobNimbus → 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 JobNimbus 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 JobNimbus 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 JobNimbus 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.

---

JobNimbus API at a glance

These values come from the JobNimbus API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the JobNimbus API?

All requests to the JobNimbus API must be authenticated using an API key passed as a Bearer token in the 'Authorization' header.

1. Get your credentials

To obtain your JobNimbus API key: 1. Log in to your JobNimbus account. 2. Click your profile icon or initials in the top right corner and select 'Settings'. 3. In the left-hand navigation menu, open the 'API' tab. 4. Click the 'New API Key' button in the top right. 5. Assign an Access Profile (which determines the permissions for the key) and provide a name or description for the integration. 6. Save the settings. Copy the generated API key string immediately, as it will not be displayed again.

2. Add them to .dlt/secrets.toml

Copy
[sources.jobnimbus_source]
api_key = "your_bearer_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 JobNimbus data can I load into DuckDB?

These are the JobNimbus endpoints dlt can load into DuckDB:

---

How do I load only new JobNimbus records?

JobNimbus exposes date_updated on jobs, 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": "jobs", "endpoint": {
    "path": "jobs",
    "data_selector": "results",
    "incremental": {"cursor_path": "date_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 JobNimbus pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading contacts and jobs from the JobNimbus API into DuckDB:

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

@dlt.source
def jobnimbus_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.jobnimbus.com/api1/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "jobs", "endpoint": {"path": "jobs", "data_selector": "results"}},
            {"name": "tasks", "endpoint": {"path": "tasks", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_jobnimbus_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="jobnimbus_pipeline",
        destination="duckdb",
        dataset_name="jobnimbus_data",
    )
    load_info = pipeline.run(jobnimbus_source())
    print(load_info)


if __name__ == "__main__":
    load_jobnimbus_to_duckdb()

Run it with python jobnimbus_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 JobNimbus 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("jobnimbus_pipeline").dataset()
df = data.jobs.df()
print(df.head())

SQL:

Copy
SELECT * FROM jobnimbus_data.jobs LIMIT 10;

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

---

How do I deploy the JobNimbus 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 JobNimbus 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 JobNimbus 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