diff --git a/scripts/jobs/parking/parking_cycle_hangars_denormalisation.py b/scripts/jobs/parking/parking_cycle_hangars_denormalisation.py
index a157d92e9..457d58583 100644
--- a/scripts/jobs/parking/parking_cycle_hangars_denormalisation.py
+++ b/scripts/jobs/parking/parking_cycle_hangars_denormalisation.py
@@ -8,11 +8,11 @@
 from scripts.helpers.helpers import get_glue_env_var, create_pushdown_predicate_for_max_date_partition_value
 environment = get_glue_env_var("environment")
 
-def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFrame:
+def spark_sql_query(glue_context, query, mapping, transformation_ctx) -> DynamicFrame:
     for alias, frame in mapping.items():
         frame.toDF().createOrReplaceTempView(alias)
     result = spark.sql(query)
-    return DynamicFrame.fromDF(result, glueContext, transformation_ctx)
+    return DynamicFrame.fromDF(result, glue_context, transformation_ctx)
 
 SqlQuery0 = '''
 WITH HangarTypes as (
@@ -109,15 +109,15 @@ def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFra
 args = getResolvedOptions(sys.argv, ['JOB_NAME'])
 
 sc = SparkContext()
-glueContext = GlueContext(sc)
-spark = glueContext.spark_session
-job = Job(glueContext)
+glue_context = GlueContext(sc)
+spark = glue_context.spark_session
+job = Job(glue_context)
 job.init(args['JOB_NAME'], args)
 ## @type: DataSource
 ## @args: [database = "dataplatform-" + environment + "-liberator-raw-zone", table_name = "liberator_licence_party", transformation_ctx = "DataSource0"]
 ## @return: DataSource0
 ## @inputs: []
-DataSource0 = glueContext.create_dynamic_frame.from_catalog(
+DataSource0 = glue_context.create_dynamic_frame.from_catalog(
     database = "dataplatform-" + environment + "-liberator-raw-zone", 
     table_name = "liberator_licence_party", 
     transformation_ctx = "DataSource0", 
@@ -127,7 +127,7 @@ def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFra
 ## @args: [database = "dataplatform-" + environment + "-liberator-raw-zone", table_name = "liberator_hangar_allocations", transformation_ctx = "DataSource3"]
 ## @return: DataSource3
 ## @inputs: []
-DataSource3 = glueContext.create_dynamic_frame.from_catalog(
+DataSource3 = glue_context.create_dynamic_frame.from_catalog(
     database = "dataplatform-" + environment + "-liberator-raw-zone", 
     table_name = "liberator_hangar_allocations", 
     push_down_predicate = create_pushdown_predicate_for_max_date_partition_value("dataplatform-" + environment + "-liberator-raw-zone", "liberator_hangar_allocations", 'import_date'),
@@ -136,7 +136,7 @@ def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFra
 ## @args: [database = "dataplatform-" + environment + "-liberator-raw-zone", table_name = "liberator_hangar_types", transformation_ctx = "DataSource2"]
 ## @return: DataSource2
 ## @inputs: []
-DataSource2 = glueContext.create_dynamic_frame.from_catalog(
+DataSource2 = glue_context.create_dynamic_frame.from_catalog(
     database = "dataplatform-" + environment + "-liberator-raw-zone", 
     table_name = "liberator_hangar_types", 
     push_down_predicate = create_pushdown_predicate_for_max_date_partition_value("dataplatform-" + environment + "-liberator-raw-zone", "liberator_hangar_types", 'import_date'),
@@ -145,7 +145,7 @@ def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFra
 ## @args: [database = "dataplatform-" + environment + "-liberator-raw-zone", table_name = "liberator_hangar_details", transformation_ctx = "DataSource1"]
 ## @return: DataSource1
 ## @inputs: []
-DataSource1 = glueContext.create_dynamic_frame.from_catalog(
+DataSource1 = glue_context.create_dynamic_frame.from_catalog(
     database = "dataplatform-" + environment + "-liberator-raw-zone", 
     table_name = "liberator_hangar_details", 
     push_down_predicate = create_pushdown_predicate_for_max_date_partition_value("dataplatform-" + environment + "-liberator-raw-zone", "liberator_hangar_details", 'import_date'),
@@ -154,12 +154,12 @@ def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFra
 ## @args: [sqlAliases = {"liberator_hangar_details": DataSource1, "liberator_hangar_types": DataSource2, "liberator_hangar_allocations": DataSource3, "liberator_licence_party": DataSource0}, sqlName = SqlQuery0, transformation_ctx = "Transform0"]
 ## @return: Transform0
 ## @inputs: [dfc = DataSource1,DataSource2,DataSource3,DataSource0]
-Transform0 = sparkSqlQuery(glueContext, query = SqlQuery0, mapping = {"liberator_hangar_details": DataSource1, "liberator_hangar_types": DataSource2, "liberator_hangar_allocations": DataSource3, "liberator_licence_party": DataSource0}, transformation_ctx = "Transform0")
+Transform0 = spark_sql_query(glue_context, query = SqlQuery0, mapping = {"liberator_hangar_details": DataSource1, "liberator_hangar_types": DataSource2, "liberator_hangar_allocations": DataSource3, "liberator_licence_party": DataSource0}, transformation_ctx = "Transform0")
 ## @type: DataSink
 ## @args: [connection_type = "s3", catalog_database_name = "dataplatform-" + environment + "-liberator-refined-zone", format = "glueparquet", connection_options = {"path": "s3://dataplatform-" + environment + "-refined-zone/parking/liberator/parking_cycle_hangars_denormalisation/", "partitionKeys": ["import_year" ,"import_month" ,"import_day" ,"import_date"], "enableUpdateCatalog":true, "updateBehavior":"UPDATE_IN_DATABASE"}, catalog_table_name = "parking_cycle_hangars_denormalisation", transformation_ctx = "DataSink0"]
 ## @return: DataSink0
 ## @inputs: [frame = Transform0]
-DataSink0 = glueContext.getSink(path = "s3://dataplatform-" + environment + "-refined-zone/parking/liberator/parking_cycle_hangars_denormalisation/", connection_type = "s3", updateBehavior = "UPDATE_IN_DATABASE", partitionKeys = ["import_year","import_month","import_day","import_date"], enableUpdateCatalog = True, transformation_ctx = "DataSink0")
+DataSink0 = glue_context.getSink(path = "s3://dataplatform-" + environment + "-refined-zone/parking/liberator/parking_cycle_hangars_denormalisation/", connection_type = "s3", updateBehavior = "UPDATE_IN_DATABASE", partitionKeys = ["import_year","import_month","import_day","import_date"], enableUpdateCatalog = True, transformation_ctx = "DataSink0")
 DataSink0.setCatalogInfo(catalogDatabase = "dataplatform-" + environment + "-liberator-refined-zone",catalogTableName = "parking_cycle_hangars_denormalisation")
 DataSink0.setFormat("glueparquet")
 DataSink0.writeFrame(Transform0)