Pier Finance Python API Docs | dltHub

Pier Finance is an API platform that provides lending, payments, and credit facility services (loan origination, payments, compliance/coverage checks, and facility management) via REST endpoints. The REST API base URL is https://sandbox.pier-finance.com/api (sandbox) and https://production.pier-finance.com/api (production) and All requests require a Bearer JWT token obtained from the /token endpoint..

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 Pier Finance data in under 10 minutes.

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What data can I load from Pier Finance?

Here are some of the endpoints you can load from Pier Finance:

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How do I authenticate with the Pier Finance API?

Obtain a JWT by POSTing client_id and secret to POST /token; include the token in Authorization: Bearer for all subsequent requests.

1. Get your credentials

1. Request sandbox access or create an account via Pier (see Request Sandbox Access). 2) In the Pier dashboard or onboarding flow obtain your client_id and secret (sandbox keys are prefixed test_, production keys prod_). 3) Use these in a POST to /token to receive a JWT; store the token for Authorization header use.

2. Add them to .dlt/secrets.toml

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[sources.pier_finance_source]
client_id = "your_client_id_here"
secret = "your_secret_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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

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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:

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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 Pier Finance 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:

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python pier_finance_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline pier_finance_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.

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Python pipeline example

This example loads coverage/check_offers and payments from the Pier Finance API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def pier_finance_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://sandbox.pier-finance.com/api (sandbox) and https://production.pier-finance.com/api (production)",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "payments", "endpoint": {"path": "payments"}},
            {"name": "coverage_check_offers", "endpoint": {"path": "coverage/check_offers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pier_finance_pipeline",
        destination="duckdb",
        dataset_name="pier_finance_data",
    )
    load_info = pipeline.run(pier_finance_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.

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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):

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import dlt

data = dlt.pipeline("pier_finance_pipeline").dataset()
sessions_df = data.payments.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pier_finance_data.payments LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pier_finance_pipeline").dataset()
data.payments.df().head()

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

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What destinations can I load Pier Finance 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.

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Troubleshooting

Authentication failures

If the /token call fails or returns 401, verify client_id and secret are correct and that you are using the correct environment (sandbox vs production). The /token endpoint returns a JSON token field; subsequent requests must send Authorization: Bearer .

Rate limits

Pier's rate limit is 1,000 requests per minute for both sandbox and production. When you receive HTTP 429, back off and retry with exponential backoff. Only retry 5xx and 429 responses.

Error format

Pier returns structured error objects with keys error_type, error_code, error_message and optional error_detail (array of objects with value, msg, param, location). Use these fields to surface user-facing messages and debugging details.

Notes and caveats: The Pier docs are an OpenAPI-driven site; many endpoints are POST-only (coverage check, payments, configuration). For GET endpoints the OpenAPI client examples (generated SDK) show list endpoints (borrowersListAllBorrowers, applications listing) returning lists in the response body — confirm the exact list key by inspecting the specific endpoint response in the OpenAPI spec or calling the endpoint in your environment.

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

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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.

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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