Reading seniority before trusting it in data-job-postings

Profiling seniority on data-job-postings before anyone builds anything on top of it.

3,500 rows, no nulls, 5 distinct values. junior, mid, senior, staff, or lead.

5 values, and they are not evenly spread: mid 33.4%, senior 28.1%, junior 21.8%, staff 9.7%. The largest takes 33.4% on its own.

Read the distinct list rather than the distinct count. Casing differences and trailing whitespace produce values that look identical in a report and group separately in SQL, and the count will not show you that — select distinct seniority order by 1 will, in about a second.

select
  count(*)                    as rows,
  count(seniority)            as present,
  count(*) - count(seniority) as nulls,
  count(distinct seniority)   as distinct_values
from data_job_postings;

Low cardinality, stable values — this is a column you can group by without thinking about it, and a reasonable candidate for a chart facet. Check the distinct list, not just the count, because a stray casing variant hides in the count and shows up in the group-by.

Where this bites: a group-by on this column produces 5 rows today. If it is a column an upstream system can add values to, it produces an unknown number tomorrow, and any dashboard laid out for 5 categories reflows without warning. Values arriving is a schema change that no schema check catches.

The grain is one row per posting, which is the context every one of those numbers depends on. None of them survive a change of grain, which is why "profile the column" and "profile the table" are the same job. Full schema, and the CSV, on the dataset page.