diff --git a/sdk/python/tests/integration/offline_store/test_universal_historical_retrieval.py b/sdk/python/tests/integration/offline_store/test_universal_historical_retrieval.py
index 3f28245f3c..e968580e8e 100644
--- a/sdk/python/tests/integration/offline_store/test_universal_historical_retrieval.py
+++ b/sdk/python/tests/integration/offline_store/test_universal_historical_retrieval.py
@@ -14,7 +14,7 @@
 from feast.infra.offline_stores.offline_utils import (
     DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL,
 )
-from feast.types import Float32, Int32
+from feast.types import Float32, Int32, String
 from feast.utils import _utc_now
 from tests.integration.feature_repos.repo_configuration import (
     construct_universal_feature_views,
@@ -639,3 +639,100 @@ def test_historical_features_containing_backfills(environment):
         actual_df,
         sort_by=["driver_id"],
     )
+
+
+@pytest.mark.integration
+@pytest.mark.universal_offline_stores
+@pytest.mark.parametrize("full_feature_names", [True, False], ids=lambda v: str(v))
+def test_historical_features_field_mapping(
+    environment, universal_data_sources, full_feature_names
+):
+    store = environment.feature_store
+
+    # (entities, datasets, data_sources) = universal_data_sources
+    # feature_views = construct_universal_feature_views(data_sources)
+
+    now = datetime.now().replace(microsecond=0, second=0, minute=0)
+    tomorrow = now + timedelta(days=1)
+    day_after_tomorrow = now + timedelta(days=2)
+
+    entity_df = pd.DataFrame(
+        data=[
+            {"driver_id": 1001, "event_timestamp": day_after_tomorrow},
+            {"driver_id": 1002, "event_timestamp": day_after_tomorrow},
+        ]
+    )
+
+    driver_stats_df = pd.DataFrame(
+        data=[
+            {
+                "id": 1001,
+                "avg_daily_trips": 20,
+                "event_timestamp": now,
+                "created": tomorrow,
+            },
+            {
+                "id": 1002,
+                "avg_daily_trips": 40,
+                "event_timestamp": tomorrow,
+                "created": now,
+            },
+        ]
+    )
+
+    expected_df = pd.DataFrame(
+        data=[
+            {
+                "driver_id": 1001,
+                "event_timestamp": day_after_tomorrow,
+                "avg_daily_trips": 20,
+            },
+            {
+                "driver_id": 1002,
+                "event_timestamp": day_after_tomorrow,
+                "avg_daily_trips": 40,
+            },
+        ]
+    )
+
+    driver_stats_data_source = environment.data_source_creator.create_data_source(
+        df=driver_stats_df,
+        destination_name=f"test_driver_stats_{int(time.time_ns())}_{random.randint(1000, 9999)}",
+        timestamp_field="event_timestamp",
+        created_timestamp_column="created",
+        # Map original "id" column to "driver_id" join key
+        field_mapping={"id": "driver_id"}
+    )
+
+    driver = Entity(name="driver", join_keys=["driver_id"])
+    driver_fv = FeatureView(
+        name="driver_stats",
+        entities=[driver],
+        schema=[
+            Field(name="driver_id", dtype=String),
+            Field(name="avg_daily_trips", dtype=Int32)
+        ],
+        source=driver_stats_data_source,
+    )
+
+    store.apply([driver, driver_fv])
+
+    offline_job = store.get_historical_features(
+        entity_df=entity_df,
+        features=["driver_stats:avg_daily_trips"],
+        full_feature_names=False,
+    )
+
+    start_time = _utc_now()
+    actual_df = offline_job.to_df()
+
+    print(f"actual_df shape: {actual_df.shape}")
+    end_time = _utc_now()
+    print(str(f"Time to execute job_from_df.to_df() = '{(end_time - start_time)}'\n"))
+
+    assert sorted(expected_df.columns) == sorted(actual_df.columns)
+    validate_dataframes(
+        expected_df,
+        actual_df,
+        sort_by=["driver_id"],
+    )