diff --git a/snuba/query/processors/logical/timeseries_processor.py b/snuba/query/processors/logical/timeseries_processor.py
index 55b4dabf55..9d8e07da9f 100644
--- a/snuba/query/processors/logical/timeseries_processor.py
+++ b/snuba/query/processors/logical/timeseries_processor.py
@@ -241,6 +241,8 @@ def extract_granularity_from_query(query: Query, column: str) -> Optional[int]:
         ),
     )
 
+    print("expr_match", expr_match)
+
     for top_expr in groupby:
         for expr in top_expr:
             result = fn_match.match(expr)
diff --git a/snuba/web/rpc/v1/endpoint_time_series.py b/snuba/web/rpc/v1/endpoint_time_series.py
index 8ffd8dbfb4..3e9d18e9e0 100644
--- a/snuba/web/rpc/v1/endpoint_time_series.py
+++ b/snuba/web/rpc/v1/endpoint_time_series.py
@@ -15,6 +15,7 @@
 
 from snuba.attribution.appid import AppID
 from snuba.attribution.attribution_info import AttributionInfo
+from snuba.cli import start
 from snuba.datasets.entities.entity_key import EntityKey
 from snuba.datasets.entities.factory import get_entity
 from snuba.datasets.pluggable_dataset import PluggableDataset
@@ -58,6 +59,10 @@
 _MAX_BUCKETS_IN_REQUEST = 1000
 
 
+def _rewind(start_timestamp: int, granularity: int) -> int:
+    return (start_timestamp // granularity) * granularity
+
+
 def _convert_result_timeseries(
     request: TimeSeriesRequest, data: list[Dict[str, Any]]
 ) -> Iterable[TimeSeries]:
@@ -124,10 +129,16 @@ def _convert_result_timeseries(
     query_duration = (
         request.meta.end_timestamp.seconds - request.meta.start_timestamp.seconds
     )
+    # start_timestamp_seconds = _rewind(
+    #     request.meta.start_timestamp.seconds, granularity=request.granularity_secs
+    # )
+    start_timestamp_seconds = request.meta.start_timestamp.seconds
     time_buckets = [
-        Timestamp(seconds=(request.meta.start_timestamp.seconds) + secs)
+        Timestamp(seconds=start_timestamp_seconds + secs)
         for secs in range(0, query_duration, request.granularity_secs)
     ]
+    print("request", request)
+    print("data", data)
 
     # this loop fill in our pre-computed dictionaries so that we can zerofill later
     for row in data:
diff --git a/tests/web/rpc/v1/test_endpoint_time_series.py b/tests/web/rpc/v1/test_endpoint_time_series.py
index 82d15efbaa..4578975fe5 100644
--- a/tests/web/rpc/v1/test_endpoint_time_series.py
+++ b/tests/web/rpc/v1/test_endpoint_time_series.py
@@ -165,6 +165,7 @@ def test_basic(self) -> None:
             assert response.status_code == 200, (error.message, error.details)
 
     def test_sum(self) -> None:
+        print(BASE_TIME.timestamp())
         # store a a test metric with a value of 1, every second of one hour
         granularity_secs = 300
         query_duration = 60 * 30
@@ -225,6 +226,72 @@ def test_sum(self) -> None:
                 ],
             ),
         ]
+        assert False
+
+    def test_rachel(self) -> None:
+        start_timestamp_seconds = 1725892950
+        # store a a test metric with a value of 1, every second of one hour
+        granularity_secs = 15
+        query_duration = 60 * 30
+        store_timeseries(
+            datetime.fromtimestamp(start_timestamp_seconds, tz=UTC),
+            1,
+            3600,
+            metrics=[DummyMetric("test_metric", get_value=lambda x: 1)],
+        )
+
+        message = TimeSeriesRequest(
+            meta=RequestMeta(
+                project_ids=[1, 2, 3],
+                organization_id=1,
+                cogs_category="something",
+                referrer="something",
+                start_timestamp=Timestamp(seconds=start_timestamp_seconds),
+                end_timestamp=Timestamp(
+                    seconds=int(start_timestamp_seconds + query_duration)
+                ),
+            ),
+            aggregations=[
+                AttributeAggregation(
+                    aggregate=Function.FUNCTION_SUM,
+                    key=AttributeKey(type=AttributeKey.TYPE_FLOAT, name="test_metric"),
+                    label="sum",
+                    extrapolation_mode=ExtrapolationMode.EXTRAPOLATION_MODE_NONE,
+                ),
+                AttributeAggregation(
+                    aggregate=Function.FUNCTION_AVG,
+                    key=AttributeKey(type=AttributeKey.TYPE_FLOAT, name="test_metric"),
+                    label="avg",
+                    extrapolation_mode=ExtrapolationMode.EXTRAPOLATION_MODE_NONE,
+                ),
+            ],
+            granularity_secs=granularity_secs,
+        )
+        response = EndpointTimeSeries().execute(message)
+        expected_buckets = [
+            Timestamp(seconds=int(BASE_TIME.timestamp()) + secs)
+            for secs in range(0, query_duration, granularity_secs)
+        ]
+        assert sorted(response.result_timeseries, key=lambda x: x.label) == [
+            TimeSeries(
+                label="avg",
+                buckets=expected_buckets,
+                data_points=[
+                    DataPoint(data=1, data_present=True)
+                    for _ in range(len(expected_buckets))
+                ],
+            ),
+            TimeSeries(
+                label="sum",
+                buckets=expected_buckets,
+                data_points=[
+                    DataPoint(data=300, data_present=True)
+                    for _ in range(len(expected_buckets))
+                ],
+            ),
+        ]
+
+        assert False
 
     def test_with_group_by(self) -> None:
         store_timeseries(