The category that eats the chart

seniority on data-job-postings has 5 values, and one of them is 33.4% of the data.

• mid — 1,170 rows, 33.4%
• senior — 984 rows, 28.1%
• junior — 764 rows, 21.8%
• staff — 339 rows, 9.7%
• lead — 243 rows, 6.9%

The top three take 83.4% between them. With only 5 values there is no tail to worry about, which makes this a genuinely easy column to chart.

select seniority,
       count(*)                                      as rows,
       round(100.0 * count(*) / sum(count(*)) over (), 1) as pct,
       round(avg(salary_min_usd)::numeric, 2)             as avg_salary_min_usd
from data_job_postings
group by 1
order by rows desc;

The second column is the one that matters. Share of rows tells you what is common; avg_salary_min_usd tells you whether the common thing is the important thing. They disagree more often than not, and a chart that shows only the first is answering the easier question.

Decide what happens to the tail before you plot it. "Other" as an explicit bucket is honest; twelve slivers is not, and neither is silently taking the top eight.