You need to define what "latency" means in your case and what is "quite high levels". We are talking about analytical data storage, it is designed for efficient batch processing. To find a single record is not a primary goal of the architecture - you will need some kind of caching/indexing for fast search. Sometimes adding "limit 1" for your single record search may solve the problem.

Be sure you are using efficent data storage format as parquet, check size of the files to be sure you don't have the ["small file problem"](https://www.royalcyber.com/blog/data-services/managing-small...), then check if you are using relevant BigQuery features. And before and after those checks run "explain" on your query, if you don't use partition keys or indexed columns your search results won't be instant in any big data system.

Ex: Recently created a big table (by materializing fact/dimension table joins and COALESCE operations) solely for analysis purposes. It sits "outside" our normal data warehouse setup mentally, but we can still maintain data quality/lineage as it exists inside dbt. Allowed us to do away with Tableau fixed calculations and cut load/group by times for end users ~95%.