diff --git a/models/marts/warehouse/dim__dates.py b/models/marts/warehouse/dim__dates.py
index d9d0a55..91a7dad 100644
--- a/models/marts/warehouse/dim__dates.py
+++ b/models/marts/warehouse/dim__dates.py
@@ -17,7 +17,7 @@ from datetime import datetime as dt, timedelta
 
 # Function to check if a given date is a weekend [Saturday(5) or Sunday(6)]
 def is_weekend(date):
-    return (date.weekday() == 5) | (date.weekday() == 6)
+    return date.weekday() in (5, 6)
 
 
 # Function to compute the in advance working days
@@ -28,14 +28,14 @@ def compute_working_days(start_date, num_working_days, holiday_list, after_cut_o
     counter = 0
 
     # For dates on weekend or holiday or after cut off time, additional 1 working day to the loop
-    if (after_cut_off == 1) | is_weekend(start_date) | (start_date in holiday_list):
+    if (after_cut_off == 1) or is_weekend(start_date) or (start_date in holiday_list):
         counter -= 1
 
     # Loop to increase n number of working days, if weekend/holiday, skip counter
     while counter < num_working_days:
         start_date = start_date + timedelta(days=1)
 
-        if is_weekend(start_date) | (start_date in holiday_list):
+        if is_weekend(start_date) or (start_date in holiday_list):
             continue
 
         counter += 1
@@ -45,36 +45,37 @@ def compute_working_days(start_date, num_working_days, holiday_list, after_cut_o
 
 # Main function
 def model(dbt, session):
-    
+
     # Setting configuration
     dbt.config(materialized="table",
                 packages = ["pandas"])
 
     # Import data from upstream dbt model
-    df_date = dbt.ref("int__dates")
+    sp_df_date = dbt.ref("int__dates")
 
-    # Extract holiday dates into a list
-    df_work = df_date.to_pandas()
-    df_new = df_work[df_work["IS_COMPANY_HOLIDAY"] == 1]
-    df_holiday_list = df_new["DATE_DAY"].tolist()
+    # Filter holiday dates into a dataframe
+    sp_df_filter = sp_df_date.filter(sp_df_date['IS_COMPANY_HOLIDAY'] == 1)
+    sp_df_holiday_date = sp_df_filter.select('DATE_DAY')
 
-    # Apply function to df
-    df_work["ADD_1_WORKING_DAY_INCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 1, df_holiday_list, 0), axis=1)
-    df_work["ADD_2_WORKING_DAY_INCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 2, df_holiday_list, 0), axis=1)
-    df_work["ADD_3_WORKING_DAY_INCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 3, df_holiday_list, 0), axis=1)
-    df_work["ADD_7_WORKING_DAY_INCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 7, df_holiday_list, 0), axis=1)
-    df_work["ADD_30_WORKING_DAY_INCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 30, df_holiday_list, 0), axis=1)
-    df_work["ADD_90_WORKING_DAY_INCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 90, df_holiday_list, 0), axis=1)
-    df_work["ADD_365_WORKING_DAY_INCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 365, df_holiday_list, 0), axis=1)
+    # Convert snowpark dataframe to pandas dataframe
+    pd_df_date = sp_df_date.to_pandas()
+    pd_df_holiday_date = sp_df_holiday_date.to_pandas()
+    
+    # Store holiday dates in a list
+    holiday_list = pd_df_holiday_date['DATE_DAY'].tolist()
 
-    # For orders after cut off time 4pm
-    # Affects only the weekdays
-    df_work["ADD_1_WORKING_DAY_EXCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 1, df_holiday_list, 1), axis=1)
-    df_work["ADD_2_WORKING_DAY_EXCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 2, df_holiday_list, 1), axis=1)
-    df_work["ADD_3_WORKING_DAY_EXCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 3, df_holiday_list, 1), axis=1)
-    df_work["ADD_7_WORKING_DAY_EXCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 7, df_holiday_list, 1), axis=1)
-    df_work["ADD_30_WORKING_DAY_EXCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 30, df_holiday_list, 1), axis=1)
-    df_work["ADD_90_WORKING_DAY_EXCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 90, df_holiday_list, 1), axis=1)
-    df_work["ADD_365_WORKING_DAY_EXCLUDED_BASE_DATE"] = df_work.apply(lambda row: compute_working_days(row["DATE_DAY"], 365, df_holiday_list, 1), axis=1)
+    # Applying add working days function to df
+    add_working_days = [1, 2, 3, 7, 30, 90 ,365]
 
-    return df_work
\ No newline at end of file
+    for working_day in add_working_days:
+        for after_cut_off in (0, 1):
+
+            if after_cut_off == 0:
+                col_name = f'ADD_{working_day}_WORKING_DAY_INCLUDED_BASE_DATE'
+            else:
+                col_name = f'ADD_{working_day}_WORKING_DAY_EXCLUDED_BASE_DATE'
+
+            # Apply function and append new calculated columns in the dataframe
+            pd_df_date[col_name] = pd_df_date['DATE_DAY'].apply(lambda start_date: compute_working_days(start_date, working_day, holiday_list, after_cut_off))
+
+    return pd_df_date
\ No newline at end of file