diff --git a/airflow/plugins/operators/scrape_state_geoportal.py b/airflow/plugins/operators/scrape_state_geoportal.py
index 62e57df18c..3e9387d88c 100644
--- a/airflow/plugins/operators/scrape_state_geoportal.py
+++ b/airflow/plugins/operators/scrape_state_geoportal.py
@@ -193,34 +193,30 @@ def execute(self, **kwargs):
         df = pd.json_normalize(api_content)
 
         if self.product == "state_highway_network":
-            # Select and rename columns
-            columns = {
-                "properties.Route": "Route",
-                "properties.County": "County",
-                "properties.District": "District",
-                "properties.RouteType": "RouteType",
-                "properties.Direction": "Direction",
-                "geometry.type": "type",
-                "geometry.coordinates": "coordinates",
-            }
-            df = df[list(columns.keys())].rename(columns=columns)
+            # Select columns to keep
+            df = df[
+                [
+                    "properties.Route",
+                    "properties.County",
+                    "properties.District",
+                    "properties.RouteType",
+                    "properties.Direction",
+                    "geometry.type",
+                    "geometry.coordinates",
+                ]
+            ]
+
+            # Dynamically create a mapping by removing known prefixes
+            columns = {col: col.split(".")[-1] for col in df.columns}
+
+            # Rename columns using the dynamically created mapping
+            df = df.rename(columns=columns)
 
             # Create new column with WKT format
             df["wkt_coordinates"] = df.apply(
                 lambda row: to_wkt(row["type"], row["coordinates"]), axis=1
             )
 
-            # Select final columns for output
-            final_columns = [
-                "Route",
-                "County",
-                "District",
-                "RouteType",
-                "Direction",
-                "wkt_coordinates",
-            ]
-            df = df[final_columns]
-
         # Compress the DataFrame content and save it
         self.gzipped_content = gzip.compress(
             df.to_json(orient="records", lines=True).encode()