diff --git a/merlin/dag/executors.py b/merlin/dag/executors.py
index fcb8c9535..ba2550ee1 100644
--- a/merlin/dag/executors.py
+++ b/merlin/dag/executors.py
@@ -389,19 +389,26 @@ def transform(
                 if col_dtype:
                     output_dtypes[col_name] = md.dtype(col_dtype).to_numpy
 
-        if isinstance(output_dtypes, dict) and isinstance(ddf._meta, pd.DataFrame):
-            dtypes = output_dtypes
-            output_dtypes = type(ddf._meta)({k: [] for k in columns})
-            for col_name, col_dtype in dtypes.items():
-                output_dtypes[col_name] = output_dtypes[col_name].astype(col_dtype)
+        def make_empty(df, cols):
+            # Construct an empty DataFrame
 
-        elif not output_dtypes:
             # TODO: constructing meta like this loses dtype information on the ddf
             # and sets it all to 'float64'. We should propagate dtype information along
             # with column names in the columngroup graph. This currently only
             # happens during intermediate 'fit' transforms, so as long as statoperators
             # don't require dtype information on the DDF this doesn't matter all that much
-            output_dtypes = type(ddf._meta)({k: [] for k in columns})
+            return df._constructor(
+                {col: df._constructor_sliced([], dtype="float64") for col in cols}
+            )
+
+        if isinstance(output_dtypes, dict) and isinstance(ddf._meta, pd.DataFrame):
+            dtypes = output_dtypes
+            output_dtypes = make_empty(ddf._meta, columns)
+            for col_name, col_dtype in dtypes.items():
+                output_dtypes[col_name] = output_dtypes[col_name].astype(col_dtype)
+
+        elif not output_dtypes:
+            output_dtypes = make_empty(ddf._meta, columns)
 
         return ensure_optimize_dataframe_graph(
             ddf=ddf.map_partitions(