Teamtailor Python API Docs | dltHub

Teamtailor is a recruitment ATS and employer branding platform offering a JSON:API‑compatible REST API for jobs, candidates, users and related resources. The REST API base URL is https://api.teamtailor.com (EU) and https://api.na.teamtailor.com (US West) and All requests require an API key provided in the Authorization header (Token token=... or Bearer for Partner API)..

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 Teamtailor data in under 10 minutes.

---

What data can I load from Teamtailor?

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

---

How do I authenticate with the Teamtailor API?

API keys are passed in the Authorization header, e.g., Authorization: Token token=YOUR_KEY (or Authorization: Bearer YOUR_KEY for the Partner API). Some endpoints also require the X-Api-Version header.

1. Get your credentials

1. Log into the Teamtailor web app as a Company Admin.\n2) Navigate to Settings → Integrations → API keys.\n3) Click + New API Key.\n4) Choose the key type (Public, Internal, Admin) and set the desired permissions (read/write).\n5) Save and copy the generated key (it cannot be viewed again later).

2. Add them to .dlt/secrets.toml

Copy
[sources.teamtailor_source]
api_key = "your_teamtailor_api_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Teamtailor 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:

Copy
python teamtailor_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline teamtailor_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.

---

Python pipeline example

This example loads jobs and job_applications from the Teamtailor API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def teamtailor_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.teamtailor.com (EU) and https://api.na.teamtailor.com (US West)",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "jobs", "endpoint": {"path": "v1/jobs", "data_selector": "data"}},
            {"name": "job_applications", "endpoint": {"path": "v1/job-applications", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="teamtailor_pipeline",
        destination="duckdb",
        dataset_name="teamtailor_data",
    )
    load_info = pipeline.run(teamtailor_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.

---

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):

Copy
import dlt

data = dlt.pipeline("teamtailor_pipeline").dataset()
sessions_df = data.jobs.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM teamtailor_data.jobs LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("teamtailor_pipeline").dataset()
data.jobs.df().head()

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

---

What destinations can I load Teamtailor 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.

---

Troubleshooting

Authentication failures

Ensure the Authorization header contains a valid API key (Token token=YOUR_KEY or Bearer YOUR_KEY). A 401 response indicates a missing or invalid key, while a 403 signals insufficient permissions.

Rate limits

Teamtailor allows roughly 50 requests per 10 seconds. Exceeding this returns HTTP 429. Monitor the X-Rate-Limit-* headers and implement exponential back‑off.

Pagination & large datasets

List endpoints return a top‑level data array with meta and links for pagination. Use page[size] (max 30) and the next/prev links or page[after]/page[before] parameters to page through results. Large unpaginated requests may produce 500 errors.

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

---

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.

Copy
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