diff --git a/linkage_dag.py b/linkage_dag.py
index 20c0359..adf3a8b 100644
--- a/linkage_dag.py
+++ b/linkage_dag.py
@@ -41,6 +41,7 @@
     get_post_success,
 )
 from dataloader.airflow_utils.utils import clear_gcs_dir
+from dataloader.scripts.clean_backups import clean_backups
 from dataloader.scripts.populate_documentation import update_table_descriptions
 
 production_dataset = "literature"
@@ -629,18 +630,23 @@
 
     # We're done! Checks passed, so copy to production and post success to slack
     start_production_cp = DummyOperator(task_id="start_production_cp")
+    update_archive = PythonOperator(
+        task_id="update_archive",
+        op_kwargs={"dataset": backup_dataset, "backup_prefix": production_dataset},
+        python_callable=clean_backups,
+    )
     success_alert = get_post_success("Article linkage update succeeded!", dag)
+    trigger_org_fixes = TriggerDagRunOperator(
+        task_id="trigger_org_fixes",
+        trigger_dag_id="org_fixes",
+    )
+
     curr_date = datetime.now().strftime("%Y%m%d")
     with open(
         f"{os.environ.get('DAGS_FOLDER')}/schemas/{gcs_folder}/table_descriptions.json"
     ) as f:
         table_desc = json.loads(f.read())
 
-    trigger_org_fixes = TriggerDagRunOperator(
-        task_id="trigger_org_fixes",
-        trigger_dag_id="org_fixes",
-    )
-
     for table in production_tables:
         push_to_production = BigQueryToBigQueryOperator(
             task_id="copy_" + table.lower(),
@@ -670,6 +676,7 @@
             >> push_to_production
             >> snapshot
             >> pop_descriptions
+            >> update_archive
             >> success_alert
             >> trigger_org_fixes
         )
@@ -691,7 +698,7 @@
         create_disposition="CREATE_IF_NEEDED",
         write_disposition="WRITE_TRUNCATE",
     )
-    start_production_cp >> copy_cld2 >> snapshot_cld2 >> success_alert
+    start_production_cp >> copy_cld2 >> snapshot_cld2 >> update_archive
 
     # task structure
     clear_tmp_dir >> metadata_sequences_start