Load Upwork data to DuckDB

Upwork provides a GraphQL API for its platform services, having decommissioned its legacy REST API surface. Everything needed to build a working Upwork → 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 Upwork 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 Upwork 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 Upwork 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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Upwork API at a glance

These values come from the Upwork API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Upwork API?

Upwork utilizes OAuth 2.0. API requests are authenticated by providing the access token in the Authorization header using the Bearer scheme.

1. Get your credentials

To obtain Upwork API credentials, follow these steps: 1. Ensure your Upwork account is fully verified (ID verification, active payment method). 2. Navigate to the Upwork Developer Portal at https://www.upwork.com/developer. 3. Locate and select the 'Request API keys' or apply page at https://www.upwork.com/developer/keys/apply. 4. Complete the application form by providing details about your intended use case (personal vs. third-party integration) and specifying whether you are a client, agency owner, or developer. 5. Submit your request. Upwork will review your application and notify you via email within approximately two weeks. Upon approval, you will be issued a Client ID and Client Secret, which you can access through the API Center in your account dashboard.

2. Add them to .dlt/secrets.toml

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[sources.upwork_source]
upwork_client_id = "your_client_id_here"
upwork_client_secret = "your_client_secret_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.

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What Upwork data can I load into DuckDB?

These are the Upwork endpoints dlt can load into DuckDB:

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How do I load only new Upwork records?

Upwork exposes offset on api/v3/jobs/search, 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": "jobs", "endpoint": {
    "path": "api/v3/jobs/search",
    "data_selector": "jobs",
    "incremental": {"cursor_path": "offset", "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 Upwork pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading oauth2/token and graphql from the Upwork API into DuckDB:

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

@dlt.source
def upwork_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.upwork.com/api",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "jobs", "endpoint": {"path": "api/v3/jobs/search", "data_selector": "jobs"}},
            {"name": "graphql", "endpoint": {"path": "graphql", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_upwork_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="upwork_pipeline",
        destination="duckdb",
        dataset_name="upwork_data",
    )
    load_info = pipeline.run(upwork_source())
    print(load_info)


if __name__ == "__main__":
    load_upwork_to_duckdb()

Run it with python upwork_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 Upwork 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("upwork_pipeline").dataset()
df = data.jobs.df()
print(df.head())

SQL:

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SELECT * FROM upwork_data.jobs LIMIT 10;

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

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How do I deploy the Upwork 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 Upwork 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 Upwork 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