diff --git a/sdk/python/feast/infra/offline_stores/bigquery.py b/sdk/python/feast/infra/offline_stores/bigquery.py
index f331d1b7685..ff985788495 100644
--- a/sdk/python/feast/infra/offline_stores/bigquery.py
+++ b/sdk/python/feast/infra/offline_stores/bigquery.py
@@ -224,31 +224,51 @@ def to_df(self):
         df = self.client.query(self.query).to_dataframe(create_bqstorage_client=True)
         return df
 
-    def to_bigquery(self, dry_run=False) -> Optional[str]:
+    def to_sql(self) -> str:
+        """
+        Returns the SQL query that will be executed in BigQuery to build the historical feature table.
+        """
+        return self.query
+
+    def to_bigquery(self, job_config: bigquery.QueryJobConfig = None) -> Optional[str]:
+        """
+        Triggers the execution of a historical feature retrieval query and exports the results to a BigQuery table.
+
+        Args:
+            job_config: An optional bigquery.QueryJobConfig to specify options like destination table, dry run, etc.
+
+        Returns:
+            Returns the destination table name or returns None if job_config.dry_run is True.
+        """
+
         @retry(wait=wait_fixed(10), stop=stop_after_delay(1800), reraise=True)
         def _block_until_done():
             return self.client.get_job(bq_job.job_id).state in ["PENDING", "RUNNING"]
 
-        today = date.today().strftime("%Y%m%d")
-        rand_id = str(uuid.uuid4())[:7]
-        dataset_project = self.config.offline_store.project_id or self.client.project
-        path = f"{dataset_project}.{self.config.offline_store.dataset}.historical_{today}_{rand_id}"
-        job_config = bigquery.QueryJobConfig(destination=path, dry_run=dry_run)
-        bq_job = self.client.query(self.query, job_config=job_config)
-
-        if dry_run:
-            print(
-                "This query will process {} bytes.".format(bq_job.total_bytes_processed)
+        if not job_config:
+            today = date.today().strftime("%Y%m%d")
+            rand_id = str(uuid.uuid4())[:7]
+            dataset_project = (
+                self.config.offline_store.project_id or self.client.project
             )
-            return None
+            path = f"{dataset_project}.{self.config.offline_store.dataset}.historical_{today}_{rand_id}"
+            job_config = bigquery.QueryJobConfig(destination=path)
+
+        bq_job = self.client.query(self.query, job_config=job_config)
 
         _block_until_done()
 
         if bq_job.exception():
             raise bq_job.exception()
 
-        print(f"Done writing to '{path}'.")
-        return path
+        if job_config.dry_run:
+            print(
+                "This query will process {} bytes.".format(bq_job.total_bytes_processed)
+            )
+            return None
+
+        print(f"Done writing to '{job_config.destination}'.")
+        return str(job_config.destination)
 
 
 @dataclass(frozen=True)
diff --git a/sdk/python/tests/test_historical_retrieval.py b/sdk/python/tests/test_historical_retrieval.py
index 656dd589e9c..08fb1d206d5 100644
--- a/sdk/python/tests/test_historical_retrieval.py
+++ b/sdk/python/tests/test_historical_retrieval.py
@@ -438,29 +438,13 @@ def test_historical_features_from_bigquery_sources(
             ],
         )
 
-        # Just a dry run, should not create table
-        bq_dry_run = job_from_sql.to_bigquery(dry_run=True)
-        assert bq_dry_run is None
-
-        bq_temp_table_path = job_from_sql.to_bigquery()
-        assert bq_temp_table_path.split(".")[0] == gcp_project
-
-        if provider_type == "gcp_custom_offline_config":
-            assert bq_temp_table_path.split(".")[1] == "foo"
-        else:
-            assert bq_temp_table_path.split(".")[1] == bigquery_dataset
-
-        # Check that this table actually exists
-        actual_bq_temp_table = bigquery.Client().get_table(bq_temp_table_path)
-        assert actual_bq_temp_table.table_id == bq_temp_table_path.split(".")[-1]
-
         start_time = datetime.utcnow()
         actual_df_from_sql_entities = job_from_sql.to_df()
         end_time = datetime.utcnow()
         with capsys.disabled():
             print(
                 str(
-                    f"\nTime to execute job_from_df.to_df() = '{(end_time - start_time)}'"
+                    f"\nTime to execute job_from_sql.to_df() = '{(end_time - start_time)}'"
                 )
             )