Yes indeed. So if you can relate the values of that to the department, then you can use the simple metric relabelling I showed originally to add the departmentID label. But you need a separate rewrite rule for each kafka_id to department mapping - so you'll have to update the config every time you add a new cluster (which you're already doing to add the new query params).
There is another approach to consider: you can make a separate set of static timeseries with the metadata bindings, like this:
kafka_cluster_info{kafka_id="lkc-0x3v22", departmentID="Engineering", env="production"} 1
kafka_cluster_info{kafka_id="lkc-0x3v25", departmentID="Accounts", env="test"} 1
...
(A static timeseries can be made using node_exporter textfile_collector, or a static web page that you scrape)
The "kafka_id" label here has to match the "kafka_id" label values in the scraped data. Then whenever you do a query on one of the main metrics, you can do a join to add the extra metadata labels, something like this:
confluent_kafka_server_retained_bytes * on (kafka_id) group_left(departmentID,env) kafka_cluster_info
Or you can do filtering on the metadata to select only the clusters belonging to a particular department or for a particular environment, e.g.
confluent_kafka_server_retained_bytes * on (kafka_id) group_left(departmentID) kafka_cluster_info{env="production"}
For the full details of this approach see:
The tradeoff here is that your queries get more complex whenever you need the departmentID or environment labels, especially in alerting rules. Adding the extra labels at scrape time keeps your queries simpler.
You can also combine both approaches: use recording rules with join queries like those above, to create new metrics with the extra labels.