diff --git a/back/scripts/workflow/data_warehouse.py b/back/scripts/workflow/data_warehouse.py
index 6aff0d159..04f0a6762 100644
--- a/back/scripts/workflow/data_warehouse.py
+++ b/back/scripts/workflow/data_warehouse.py
@@ -1,5 +1,6 @@
 from pathlib import Path
 
+import pandas as pd
 import polars as pl
 from sqlalchemy import text
 
@@ -19,6 +20,7 @@ def __init__(self, config: dict):
         self.config = config
         self.warehouse_folder = Path(self.config["warehouse"]["data_folder"])
         self.warehouse_folder.mkdir(exist_ok=True, parents=True)
+        self.chunksize = 10000
 
         self.send_to_db = {
             "collectivites": CommunitiesEnricher.get_output_path(config),
@@ -50,7 +52,7 @@ def _send_to_postgres(self):
         # or keep the same schema.
         if_table_exists = "replace" if self.config["workflow"]["replace_tables"] else "append"
 
-        with connector.engine.connect() as conn:
+        with connector.engine.begin() as conn:
             for table_name, filename in self.send_to_db.items():
                 df = pl.read_parquet(filename)
 
@@ -59,7 +61,50 @@ def _send_to_postgres(self):
                         f"SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name= '{table_name}')"
                     )
                     table_exists = conn.execute(table_exists_query).scalar()
+
                     if table_exists:
                         conn.execute(text(f"TRUNCATE {table_name}"))
+                        self.add_missing_columns_to_sql_table(conn, table_name, df)
+                    df.to_pandas().to_sql(
+                        table_name, conn, if_exists=if_table_exists, chunksize=self.chunksize
+                    )
+
+    @staticmethod
+    def add_missing_columns_to_sql_table(conn, table_name: str, df: pl.DataFrame):
+        """Ajoute les colonnes manquantes dans la table SQL à partir du DataFrame Polars."""
+
+        schema = df.schema
+        columns_sql = conn.execute(
+            text(f"""
+            SELECT column_name FROM information_schema.columns
+            WHERE table_name = '{table_name}'
+        """)
+        ).fetchall()
+        existing_cols = {col[0] for col in columns_sql}
+
+        missing_cols = schema.keys() - existing_cols
+        if not missing_cols:
+            return
+
+        # Mapping Polars -> SQL
+        type_mapping = {
+            pl.Int64: "BIGINT",
+            pl.Int32: "INTEGER",
+            pl.Float64: "DOUBLE PRECISION",
+            pl.Float32: "REAL",
+            pl.Boolean: "BOOLEAN",
+            pl.Utf8: "TEXT",
+            pl.Date: "DATE",
+            pl.Datetime: "TIMESTAMP",
+        }
+
+        if missing_cols:
+            add_columns = []
+            for col in missing_cols:
+                pl_type = schema[col]
+                sql_type = type_mapping.get(pl_type, "TEXT")
+                add_columns.append(f'ADD COLUMN "{col}" {sql_type}')
 
-                df.write_database(table_name, conn, if_table_exists=if_table_exists)
+            alter_query = f'ALTER TABLE "{table_name}" {", ".join(add_columns)};'
+            conn.execute(text(alter_query))
+            conn.commit()