Flattening JSON while also keep embedded types

[amanda3]

I'm attempting to create DLT tables from a source table that includes an "data" column that is a JSON string. I'm doing something like this: 

sales_schema = StructType([
  StructField("customer_id", IntegerType(), True),
  StructField("order_numbers", ArrayType(LongType()), True)
  StructField("data", StructType([
      StructField("value", IntegerType())
  ])
])
@dlt.table(
  schema=sales_schema)
def sales():
  df = spark.readStream.table("table_name")
     .withColumn("parsed_data", from_json(col("data"), sales_schema)
     .select("parsed_data.*")
  return df

this works to flatten, but for some reason the data column in the DLT sales table becomes a struct where "value" is no longer an Integer but a String. 

is there a way to keep the original value type?