diff --git a/graphstorm-processing/graphstorm_processing/config/config_parser.py b/graphstorm-processing/graphstorm_processing/config/config_parser.py
index 0193032b0c..44bc018b46 100644
--- a/graphstorm-processing/graphstorm_processing/config/config_parser.py
+++ b/graphstorm-processing/graphstorm_processing/config/config_parser.py
@@ -25,6 +25,7 @@
     BucketNumericalFeatureConfig,
     MultiNumericalFeatureConfig,
     NumericalFeatureConfig,
+    RankGaussNumericalFeatureConfig,
 )
 from .data_config_base import DataStorageConfig
 
@@ -62,6 +63,8 @@ def parse_feat_config(feature_dict: Dict) -> FeatureConfig:
         return MultiNumericalFeatureConfig(feature_dict)
     elif transformation_name == "bucket-numerical":
         return BucketNumericalFeatureConfig(feature_dict)
+    elif transformation_name == "rank-gauss":
+        return RankGaussNumericalFeatureConfig(feature_dict)
     else:
         raise RuntimeError(f"Unknown transformation name: '{transformation_name}'")
 
diff --git a/graphstorm-processing/graphstorm_processing/config/numerical_configs.py b/graphstorm-processing/graphstorm_processing/config/numerical_configs.py
index 6544d5f14e..025c63dbd9 100644
--- a/graphstorm-processing/graphstorm_processing/config/numerical_configs.py
+++ b/graphstorm-processing/graphstorm_processing/config/numerical_configs.py
@@ -143,3 +143,49 @@ def _sanity_check(self) -> None:
         assert (
             isinstance(self.slide_window_size, numbers.Number) or self.slide_window_size == "none"
         ), f"Expect no slide window size or expect {self.slide_window_size} is a number"
+
+
+class RankGaussNumericalFeatureConfig(FeatureConfig):
+    """Feature configuration for rank gauss numerical features.
+
+    Supported kwargs
+    ----------------
+    imputer: str
+        A method to fill in missing values in the data. Valid values are:
+        "none" (Default), "mean", "median", and "most_frequent". Missing values will be replaced
+        with the respective value computed from the data.
+
+    normalizer: str
+        A normalization to apply to each column. Valid values are
+        "none", "min-max", and "standard".
+
+        The transformation applied will be:
+
+        * "none": (Default) Don't normalize the numerical values during encoding.
+        * "min-max": Normalize each value by subtracting the minimum value from it,
+        and then dividing it by the difference between the maximum value and the minimum.
+        * "standard": Normalize each value by dividing it by the sum of all the values.
+
+    epsilon: float
+        Epsilon for normalization used to avoid INF float during computation.
+    """
+
+    def __init__(self, config: Mapping):
+        super().__init__(config)
+        self.imputer = self._transformation_kwargs.get("imputer", "none")
+        self.norm = self._transformation_kwargs.get("normalizer", "none")
+        self.epsilon = self._transformation_kwargs.get("epsilon", "none")
+
+        self._sanity_check()
+
+    def _sanity_check(self) -> None:
+        super()._sanity_check()
+        assert (
+            self.imputer in VALID_IMPUTERS
+        ), f"Unknown imputer requested, expected one of {VALID_IMPUTERS}, got {self.imputer}"
+        assert (
+            self.norm in VALID_NORMALIZERS
+        ), f"Unknown normalizer requested, expected one of {VALID_NORMALIZERS}, got {self.norm}"
+        assert (
+            self.epsilon == "none" or (isinstance(self.epsilon, numbers.Number) and 0.0 <= self.epsilon <= 1.0)
+        ), f"Expect epsilon {self.epsilon} be in range [0, 1], or not provided"
\ No newline at end of file