Load Jobber data to DuckDB

Jobber is a platform for home service businesses that provides a GraphQL API for building applications and integrations. Everything needed to build a working Jobber → 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.

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Build your Jobber 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 Jobber 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 Jobber 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.

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Jobber API at a glance

These values come from the Jobber API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Jobber API?

Authentication is handled via OAuth 2.0. Every request must include an 'Authorization' header with a Bearer token, a 'X-JOBBER-GRAPHQL-VERSION' header specifying the API version, and a 'Content-Type: application/json' header.

1. Get your credentials

Jobber does not use static API keys for authentication. Instead, it utilizes an OAuth 2.0 authorization code flow. To obtain credentials: 1. Sign up for a developer account at the Jobber Developer Center. 2. Create a new app within the Developer Center dashboard to receive your Client ID and Client Secret. 3. Implement the OAuth 2.0 flow: use the Client ID to redirect users to Jobber's authorization endpoint, where they grant your app access to their data. 4. Upon user approval, exchange the authorization code at the token endpoint (along with your Client Secret) to receive an access token (for API requests) and a refresh token (for ongoing access). Refer to Jobber's official 'App Authorization (OAuth 2.0)' documentation for the full implementation steps.

2. Add them to .dlt/secrets.toml

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[sources.jobber_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
# Note: Access tokens are generated dynamically via OAuth; # do not store them as static keys in secrets.toml.

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.

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What Jobber data can I load into DuckDB?

These are the Jobber endpoints dlt can load into DuckDB:

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How do I load only new Jobber records?

Jobber exposes pageInfo.endCursor on api/graphql, 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.

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{"name": "clients", "endpoint": {
    "path": "api/graphql",
    "data_selector": "data.clients.nodes",
    "incremental": {"cursor_path": "pageInfo.endCursor", "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.

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What does the generated Jobber pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/oauth/authorize and /api/graphql from the Jobber API into DuckDB:

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

@dlt.source
def jobber_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.getjobber.com/api/graphql",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "clients", "endpoint": {"path": "api/graphql", "data_selector": "data.clients.nodes"}},
            {"name": "jobs", "endpoint": {"path": "api/graphql", "data_selector": "data.jobs.nodes"}}
        ],
    }
    yield from rest_api_resources(config)


def load_jobber_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="jobber_pipeline",
        destination="duckdb",
        dataset_name="jobber_data",
    )
    load_info = pipeline.run(jobber_source())
    print(load_info)


if __name__ == "__main__":
    load_jobber_to_duckdb()

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

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How do I query Jobber 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):

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import dlt

data = dlt.pipeline("jobber_pipeline").dataset()
df = data.api/graphql.df()
print(df.head())

SQL:

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SELECT * FROM jobber_data.api/graphql LIMIT 10;

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

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How do I deploy the Jobber 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 Jobber loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Jobber 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.

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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