Is there a way to use Iceberg tables directly in Python models?

[lorenzlorg]

I am using dbt with Snowflake and Python models (Snowpark) for data transformations. My goal is to create Iceberg tables directly from Python models.

Currently, dbt only supports Iceberg tables for SQL models. Python models always materialize as normal Snowflake tables, and there is no way to set table_format='iceberg' in a Python model.

Is there any way to materialize Iceberg tables directly from a Python model in dbt?

If not, what are the recommended alternative patterns to achieve the same goal while keeping the logic in Python?
For example, can pre-hooks, post-hooks, or staging tables be used in a reliable way?

I want to avoid intermediate staging tables if possible