Firebase BigQuery Export Campaign Performance - firebase

Question

I'm having a hard time replicating the metrics found when accessing in the Analytics console. Particularly user and session metrics when split by campaign details (extracted from 'campaign_details' event and attributed on a last interaction basis over 90 days). If I query the data without considering campaign_details my values are as in the console. I'd be interested to know if anyone has worked with this previously and managed to get the data as in the console or if it's even possible to expect parity?
with initial_prep as (
SELECT
(select max(value.string_value) from unnest(user_properties) where key='store') store,
device.operating_system as operating_system,
event_date,
user_pseudo_id,
event_timestamp,
TIMESTAMP_MICROS(event_timestamp) AS ts,
LAG(TIMESTAMP_MICROS(event_timestamp)) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS prev_evt_ts,
IF(event_name = "session_start", 1, 0) AS is_session_start_event,
IF(event_name = "first_open", 1, 0) AS is_first_visit_event,
IF(event_name = "screen_view", 1, 0) AS is_screen_view,
IF(event_name = "purchase",1,0) as is_purchase,
ecommerce.purchase_revenue as value,
ecommerce.shipping_value as shipping,
ecommerce.total_item_quantity as quantity,
FROM
`[PROJECT DETAILS REDACTED].events_20*`
WHERE
parse_date('%y%m%d', _table_suffix) between DATE_sub(current_date(), interval 1 day) and DATE_sub(current_date(), interval 1 day)
and
device.operating_system = 'IOS'
), user_sources as (
select
user_pseudo_id,
TIMESTAMP_MICROS(event_timestamp) AS ts,
(select max(value.string_value) from unnest(event_params) where key='source' and event_name in( 'campaign_details')) source,
(select max(value.string_value) from unnest(event_params) where key='campaign' and event_name in( 'campaign_details')) campaign
from
`[PROJECT DETAILS REDACTED].events_20*`
WHERE
parse_date('%y%m%d', _table_suffix) between DATE_sub(current_date(), interval 90 day) and DATE_sub(current_date(), interval 1 day)
and
event_name in ('campaign_details')
)
, session_id_created as (
SELECT
*,
SUM(is_session_start_event) OVER (PARTITION BY user_pseudo_id ORDER BY ts) AS session_id
FROM initial_prep
)
, session_details as (
SELECT
si.user_pseudo_id,
store,
operating_system,
event_date,
event_timestamp,
session_id,
MAX(is_session_start_event) OVER (PARTITION BY si.user_pseudo_id, session_id) AS has_session_start_event,
is_session_start_event,
MAX(is_first_visit_event) OVER (PARTITION BY si.user_pseudo_id, session_id) AS has_first_visit_event,
is_first_visit_event,
is_screen_view,
MAX(event_timestamp) OVER (PARTITION BY si.user_pseudo_id, session_id) AS max_timestamp,
MIN(event_timestamp) OVER (PARTITION BY si.user_pseudo_id, session_id) AS min_timestamp,
is_purchase,
value,
shipping,
quantity,
us.source,
us.campaign,
row_number() over (partition by si.user_pseudo_id, event_timestamp order by us.ts desc) rank,
us.ts time_campaign
from session_id_created si
left join user_sources us on us.user_pseudo_id = si.user_pseudo_id and si.ts >= us.ts -->= timestamp_sub(us.ts,interval 3600000 MICROSECOND)
)
, session_fin as (
select user_pseudo_id,
store,
operating_system,
source,
campaign,
event_date,
session_id,
has_session_start_event,
has_first_visit_event,
max_timestamp,
min_timestamp,
sum(is_session_start_event) sessions_alt,
sum(is_screen_view) screen_views,
sum(value) revenue,
sum(is_purchase) transactions,
sum(shipping) shipping,
sum(quantity) item_quantity
from session_details
where rank =1
group by
user_pseudo_id,
store,
event_date,
operating_system,
session_id,
source,
campaign,
has_session_start_event,
has_first_visit_event,
max_timestamp,
min_timestamp
)
select store
, operating_system
, source
, campaign
, event_date applicabledate
, sum(sessions_alt) sessions
, sum(transactions) transactions
, sum(revenue) local_revenue
, sum(shipping) local_shipping
, sum(item_quantity) item_quantity
, avg(max_timestamp/100000 - min_timestamp/100000) avgsessionduration
, count(distinct user_pseudo_id) users
, count(distinct case when has_first_visit_event = 1 then user_pseudo_id end) new_users
, sum(screen_views) screenviews
from session_fin
group by store, event_date, operating_system
, source
, campaign
order by users desc
/**/