diff --git a/quinn/dataframe_validator.py b/quinn/dataframe_validator.py
index afd21c5a..386979b7 100644
--- a/quinn/dataframe_validator.py
+++ b/quinn/dataframe_validator.py
@@ -37,7 +37,28 @@ def validate_presence_of_columns(df: DataFrame, required_col_names: list[str]) -
     if missing_col_names:
         raise DataFrameMissingColumnError(error_message)
     
-def validate_schema(required_schema: StructType, ignore_nullable=False, _func=None):
+def validate_schema(
+    required_schema: StructType, 
+    ignore_nullable: bool = False, 
+    _df: DataFrame = None
+) -> function:
+    """Function that validate if a given DataFrame has a given StructType as its schema.
+    Implemented as a decorator factory so can be used both as a standalone function or as 
+    a decorator to another function.
+
+    :param required_schema: StructType required for the DataFrame
+    :type required_schema: StructType
+    :param ignore_nullable: (Optional) A flag for if nullable fields should be
+    ignored during validation
+    :type ignore_nullable: bool, optional
+    :param _df: DataFrame to validate, mandatory when called as a function. Not required
+    when called as a decorator
+    :type _df: DataFrame
+
+    :raises DataFrameMissingStructFieldError: if any StructFields from the required
+    schema are not included in the DataFrame schema
+    """
+
     def decorator(func):
         def wrapper(*args, **kwargs):
             df = func(*args, **kwargs)
@@ -59,47 +80,12 @@ def wrapper(*args, **kwargs):
             return df
         return wrapper
 
-    if _func is None:
+    if _df is None:
         # This means the function is being used as a decorator
         return decorator
     else:
         # This means the function is being called directly with a DataFrame
-        return decorator(lambda: _func)()
-
-
-def x_validate_schema(
-    df: DataFrame,
-    required_schema: StructType,
-    ignore_nullable: bool = False,
-) -> None:
-    """Function that validate if a given DataFrame has a given StructType as its schema.
-
-    :param df: DataFrame to validate
-    :type df: DataFrame
-    :param required_schema: StructType required for the DataFrame
-    :type required_schema: StructType
-    :param ignore_nullable: (Optional) A flag for if nullable fields should be
-    ignored during validation
-    :type ignore_nullable: bool, optional
-
-    :raises DataFrameMissingStructFieldError: if any StructFields from the required
-    schema are not included in the DataFrame schema
-    """
-    _all_struct_fields = copy.deepcopy(df.schema)
-    _required_schema = copy.deepcopy(required_schema)
-
-    if ignore_nullable:
-        for x in _all_struct_fields:
-            x.nullable = None
-
-        for x in _required_schema:
-            x.nullable = None
-
-    missing_struct_fields = [x for x in _required_schema if x not in _all_struct_fields]
-    error_message = f"The {missing_struct_fields} StructFields are not included in the DataFrame with the following StructFields {_all_struct_fields}"
-
-    if missing_struct_fields:
-        raise DataFrameMissingStructFieldError(error_message)
+        return decorator(lambda: _df)()
 
 
 def validate_absence_of_columns(df: DataFrame, prohibited_col_names: list[str]) -> None: