JustSift Python API Docs | dltHub

JustSift API is a REST API that provides a single searchable source of employee/people profile data, media, and advanced people search for organizations. The REST API base URL is https://api.justsift.com/v1 and All requests require a Bearer token provided as an Authorization header..

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 JustSift data in under 10 minutes.

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What data can I load from JustSift?

Here are some of the endpoints you can load from JustSift:

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How do I authenticate with the JustSift API?

Provide the API token as a Bearer token in the Authorization header: Authorization: Bearer {token}. Media image token may also be passed as a query parameter token.

1. Get your credentials

1. Sign in to your JustSift admin UI. 2) Navigate to API Access / API Keys (or Applications) in the Admin menu. 3) Click Create API Key, give it a name and assign the appropriate permissions (data/media). 4) The UI will show the REST base URL for your provisioned environment and the generated API token — copy both. 5) Store the token securely.

2. Add them to .dlt/secrets.toml

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[sources.justsift_source]
api_key = "your_api_token_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 JustSift 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 justsift_pipeline.py

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

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Pipeline justsift_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset justsift_data
The duckdb destination used duckdb:/justsift.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 justsift_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 people and search_people from the JustSift 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 justsift_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.justsift.com/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "people", "endpoint": {"path": "people/{idOrEmail}", "data_selector": "data"}},
            {"name": "search_people", "endpoint": {"path": "search/people", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="justsift_pipeline",
        destination="duckdb",
        dataset_name="justsift_data",
    )
    load_info = pipeline.run(justsift_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("justsift_pipeline").dataset()
sessions_df = data.people.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM justsift_data.people LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("justsift_pipeline").dataset()
data.people.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 JustSift 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 you receive 401 responses, verify you are using the correct API token for the same provisioned base URL shown when the key was created. The Authorization header must be: Authorization: Bearer {token}. For media requests you may instead pass the image token as ?token={token}.

Rate limiting and server errors

The docs list 500 for internal server errors. If you encounter 429 or sustained 5xx errors, implement exponential backoff and retry. Contact support@justsift.com if errors persist.

Pagination

Search endpoints return data (array) plus paging metadata fields (links, meta, next, prev, page, pageSize). Use page and pageSize query params to iterate pages; check returned links or next/prev tokens/URLs.

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