Ym-careers Python API Docs | dltHub

Ym-careers is a REST API for career site services (job search, recruiter management, lead generation, and job‑seeker activity reporting). The REST API base URL is https://api.careerwebsite.com/v1 and All requests require either an API access token (Authorization header, short‑lived) or an API access key (X‑API‑KEY header, long‑lived)..

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 Ym-careers data in under 10 minutes.

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What data can I load from Ym-careers?

Here are some of the endpoints you can load from Ym-careers:

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How do I authenticate with the Ym-careers API?

The API requires either a short‑lived API_ACCESS_TOKEN passed in the Authorization header or a long‑lived API_ACCESS_KEY passed in the X‑API‑KEY header.

1. Get your credentials

1. Contact the YM Careers integrations team (YMPartnerReps@momentivesoftware.com) or your contracted career‑center contact to request API access.
2. Obtain the required Site Name(s)/Site ID(s) and scopes (e.g., job‑search, location‑services, lead‑generation, job‑seeker‑activity).
3. Use the Create API Access Key endpoint (POST /api/api-access-key) to generate a persistent API_ACCESS_KEY, or the Create API Access Token endpoint (POST /api/api-access-token) to obtain a short‑lived API_ACCESS_TOKEN.
4. Store the API_ACCESS_KEY securely; programmatically request new tokens when needed.

2. Add them to .dlt/secrets.toml

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[sources.ym_careers_source]
api_access_key = "your_api_access_key_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 Ym-careers 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 ym_careers_pipeline.py

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

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Pipeline ym_careers_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ym_careers_data
The duckdb destination used duckdb:/ym_careers.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 ym_careers_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 job_search and jobs_applied_activity from the Ym-careers 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 ym_careers_source(api_access_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.careerwebsite.com/v1",
            "auth": {
                "type": "api_key",
                "api_access_key": api_access_key,
            },
        },
        "resources": [
            {"name": "job_search", "endpoint": {"path": "gateway/job-search", "data_selector": "data"}},
            {"name": "jobs_applied_activity", "endpoint": {"path": "careers/integration/jobs-applied-activity", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ym_careers_pipeline",
        destination="duckdb",
        dataset_name="ym_careers_data",
    )
    load_info = pipeline.run(ym_careers_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("ym_careers_pipeline").dataset()
sessions_df = data.job_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ym_careers_data.job_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ym_careers_pipeline").dataset()
data.job_search.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 Ym-careers 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 Unauthorized, ensure you are sending either Authorization: <API_ACCESS_TOKEN> (token must be valid and not expired) or X-API-KEY: <API_ACCESS_KEY>. Tokens expire ~15 minutes; create a fresh token via POST /api/api-access-token and retry. Verify that the API key matches the value returned by POST /api/api-access-key.

Rate limits and token expiry

The documentation does not publish explicit rate limits. Handle 429 Too Many Requests by backing off and retrying after a short delay. If you encounter repeated 401 responses, rotate tokens automatically.

Pagination

Several endpoints support start (offset) and rows (page size) query parameters (e.g., job-search, event‑tracking endpoints). Use these parameters to iterate through results. Responses include code and message fields; record arrays are typically under the data key.

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