diff --git a/src/snowflake/snowpark/dataframe.py b/src/snowflake/snowpark/dataframe.py
index c4dd09095fd..4e5e1867cb4 100644
--- a/src/snowflake/snowpark/dataframe.py
+++ b/src/snowflake/snowpark/dataframe.py
@@ -5689,6 +5689,57 @@ def _format_datatype(name, dtype, nullable=None, depth=0):
 
         return f"root\n{schema_tmp_str}"
 
+    def pipe(
+        self, func: Callable[..., "DataFrame"], *args: str, **kwargs: Dict[str, str]
+    ) -> "DataFrame":
+        """
+        The pipe method facilitates chainable operations by allowing the output of one function to be passed as the
+        input to another function in a sequence. It is particularly useful for creating a clean, readable workflow
+        when applying multiple transformations or operations on data. The method supports chaining by returning
+        the result of each operation, making the sequence easy to understand and maintain.
+
+        Args:
+            func: A function that takes a DataFrame as its first argument and returns a transformed DataFrame.
+            args: Positional arguments to be passed to the function.
+            kwargs: Keyword arguments to be passed to the function.
+
+        Example::
+            >>> from snowflake.snowpark import Session
+            >>> from snowflake.snowpark.functions import lit
+            >>> data = [
+            ...    {"id": 1, "value": 50},
+            ...    {"id": 2, "value": 30},
+            ...    {"id": 3, "value": 70},
+            ...    {"id": 4, "value": 20}
+            ...]
+            >>> df = session.create_dataframe(data)
+            >>> def add_column(df, column_name, value):
+            ...     return df.withColumn(column_name, F.lit(value))
+            >>> transformed_df = (
+            ...     df
+            ...     .pipe(add_column, "new_col", 42)
+            ...     .pipe(lambda d: d.filter(d["new_col"]> 40))
+            ... )
+            >>> transformed_df.show()
+        ------------------------------
+        |"ID"  |"VALUE"  |"NEW_COL"  |
+        ------------------------------
+        |1     |50       |42         |
+        |3     |70       |42         |
+        ------------------------------
+        <BLANKLINE>
+        """
+        if isinstance(func, tuple):
+            func, target = func
+            if target in kwargs:
+                raise ValueError(
+                    "%s is both the pipe target and a keyword " "argument" % target
+                )
+            kwargs[target] = self
+            return func(*args, **kwargs)
+        else:
+            return func(self, *args, **kwargs)
+
     def print_schema(self, level: Optional[int] = None) -> None:
         """
         Prints the schema of a dataframe in tree format.
diff --git a/tests/integ/test_dataframe.py b/tests/integ/test_dataframe.py
index 75d1754a762..532ae117621 100644
--- a/tests/integ/test_dataframe.py
+++ b/tests/integ/test_dataframe.py
@@ -3389,6 +3389,25 @@ def check_df_with_query_id_result_scan(session, df):
     Utils.check_answer(df, df_from_result_scan)
 
 
+def test_pipe(session):
+    data = [
+        {"id": 1, "value": 50},
+        {"id": 2, "value": 30},
+        {"id": 3, "value": 70},
+        {"id": 4, "value": 20},
+    ]
+    df = session.create_dataframe(data)
+
+    def add_column(df, column_name, value):
+        return df.withColumn(column_name, lit(value))
+
+    transformed_df = df.pipe(add_column, "new_col", 42).pipe(
+        lambda d: d.filter(d["new_col"] > 40)
+    )
+    expected_rows = [Row(ID=1, VALUE=50, NEW_COL=42), Row(ID=3, VALUE=70, NEW_COL=42)]
+    assert transformed_df.collect() == expected_rows
+
+
 @pytest.mark.xfail(
     "config.getoption('local_testing_mode', default=False)",
     reason="Result scan is a SQL feature",