diff --git a/dbt/adapters/bigquery/column.py b/dbt/adapters/bigquery/column.py
index cfa298b82..02a3e8182 100644
--- a/dbt/adapters/bigquery/column.py
+++ b/dbt/adapters/bigquery/column.py
@@ -5,6 +5,8 @@
 
 from google.cloud.bigquery import SchemaField
 
+_PARENT_DATA_TYPE_KEY = "__parent_data_type"
+
 Self = TypeVar("Self", bound="BigQueryColumn")
 
 
@@ -215,15 +217,29 @@ def _update_nested_column_data_types(
 
     if len(column_name_parts) == 1:
         # Base case: column is not nested - store its data_type concatenated with constraint if provided.
-        nested_column_data_types[root_column_name] = (
+        column_data_type_and_constraints = (
             column_data_type
             if column_rendered_constraint is None
             else f"{column_data_type} {column_rendered_constraint}"
         )
+        if root_column_name not in nested_column_data_types:
+            nested_column_data_types[root_column_name] = column_data_type_and_constraints
+        else:
+            # entry could already exist if this is a parent column -- preserve the parent data type under "_PARENT_DATA_TYPE_KEY"
+            existing_nested_column_data_type = nested_column_data_types[root_column_name]
+            assert isinstance(existing_nested_column_data_type, dict)  # keeping mypy happy
+            existing_nested_column_data_type[
+                _PARENT_DATA_TYPE_KEY
+            ] = column_data_type_and_constraints
     else:
         # Initialize nested dictionary
         if root_column_name not in nested_column_data_types:
             nested_column_data_types[root_column_name] = {}
+        elif not isinstance(nested_column_data_types[root_column_name], dict):
+            # a parent specified its base type -- preserve its data_type and potential rendered constraints
+            # this is used to specify a top-level 'struct' or 'array' field with its own description, constraints, etc
+            parent_data_type = nested_column_data_types[root_column_name]
+            nested_column_data_types[root_column_name] = {_PARENT_DATA_TYPE_KEY: parent_data_type}
 
         # Recursively process rest of remaining column name
         remaining_column_name = ".".join(column_name_parts[1:])
@@ -252,8 +268,23 @@ def _format_nested_data_type(unformatted_nested_data_type: Union[str, Dict[str,
     if isinstance(unformatted_nested_data_type, str):
         return unformatted_nested_data_type
     else:
+        parent_data_type = unformatted_nested_data_type.pop(_PARENT_DATA_TYPE_KEY, None)
+        parent_constraints = None
+        if parent_data_type:
+            parent_data_type_flat = parent_data_type.split()
+            if len(parent_data_type_flat) > 1:
+                parent_data_type = parent_data_type_flat[0]
+                parent_constraints = " ".join(parent_data_type_flat[1:])
+
         formatted_nested_types = [
             f"{column_name} {_format_nested_data_type(column_type)}"
             for column_name, column_type in unformatted_nested_data_type.items()
         ]
-        return f"""struct<{", ".join(formatted_nested_types)}>"""
+
+        formatted_nested_type = f"""struct<{", ".join(formatted_nested_types)}>"""
+        if parent_data_type and parent_data_type.lower() == "array":
+            formatted_nested_type = f"""array<{formatted_nested_type}>"""
+        if parent_constraints:
+            formatted_nested_type = f"""{formatted_nested_type} {parent_constraints}"""
+
+        return formatted_nested_type