@@ -163,6 +163,26 @@ def metadata(self) -> Optional[RetrievalMetadata]:
         """
         pass
 
+    def supports_remote_storage_export(self) -> bool:
+        """
+        This method should return True if the RetrievalJob supports `to_remote_storage()`.
+        """
+        return False
+
+    def to_remote_storage(self) -> List[str]:
+        """
+        This method should export the result of this RetrievalJob to
+        remote storage (such as S3, GCS, HDFS, etc).
+        Implementations of this method should export the results as
+        multiple parquet files, each file sized appropriately
+        depending on how much data is being returned by the retrieval
+        job.
+
+        Returns:
+            A list of parquet file paths in remote storage.
+        """
+        raise NotImplementedError()
+
 
 class OfflineStore(ABC):
     """
 
@@ -280,7 +280,7 @@ def get_expected_training_df(
 
 @pytest.mark.integration
 @pytest.mark.universal_offline_stores
-@pytest.mark.parametrize("full_feature_names", [True, False], ids=lambda v: str(v))
+@pytest.mark.parametrize("full_feature_names", [True, False], ids=lambda v: f"full:{v}")
 def test_historical_features(environment, universal_data_sources, full_feature_names):
     store = environment.feature_store
 
@@ -410,7 +410,7 @@ def test_historical_features(environment, universal_data_sources, full_feature_n
 
 
 @pytest.mark.integration
-@pytest.mark.universal
+@pytest.mark.universal_offline_stores
 @pytest.mark.parametrize("full_feature_names", [True, False], ids=lambda v: str(v))
 def test_historical_features_with_shared_batch_source(
     environment, universal_data_sources, full_feature_names