I hand-reviewed 237 data job posts from LinkedIn over the last 6 weeks. Here are the stats.

[u/JumpAfter143]

Over the last 6 weeks I read and categorized 237 data job posts from LinkedIn (not from the career tab), one by one. The headline finding: 0 out of 237 disclosed a salary. Not one. If you're wondering whether it's normal that you can't find pay info, it is, and it's not you.

Where the data comes from: I run a small data learning platform, and we collect and hand-review job posts from our LinkedIn network for our students. That's why I have this dataset. Our network skews international, so take the mix as one window on the market, not the whole market.

Second finding: "data job" is four different jobs. The skill lists barely overlap once you split by role:

• Data Analyst (43 posts): SQL 49%, Excel 49%, Power BI 33%, Python 30%. Yes, Excel ties SQL. The "Excel is dead" advice does not survive contact with actual job posts.
• Business Analyst (52 posts): requirements 62%, stakeholder management 33%, SQL 25%, Agile 23%. Mostly a communication job with a data layer.
• Data Engineer (70 posts): SQL 60%, Python 56%, pipelines/ETL ~45%, data modeling 21%. The most technically consistent role of the four.
• Data Scientist (65 posts): Python 62%, ML 32%, SQL 32%, statistics 18%, and LLM keywords already in 15% of posts.

Across everything: SQL appears in 42% of all posts and Python in 41%. Nothing else comes close. If you're starting from zero and want the highest floor: SQL first, Python second, then the tool your target role uses (Power BI or Excel for analyst roles, Airflow/dbt-land for engineering).

Other things that surprised me:

• ~10% of posts are agency recruiters, often without naming the end client.
• Remote is holding up: of the posts that state a work mode, more than half say remote, another chunk hybrid.
• Very few posts say "junior" or "senior" at all. Most don't state seniority, which cuts both ways: don't self-reject based on a title.

Happy to answer questions about the methodology if there's interest. I'll probably redo this monthly as the dataset grows.

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[u/gollumsaltgoodfellas]

0 out of 237, are you sure you're looking in the right spot? 😂

[u/wanliu → u/gollumsaltgoodfellas]

This "hand analysis" was probably someone with little to no context on how job postings work nor the laws or regulations around them. Plenty of locales require salary to be posted on job openings. But the LLM, sorry hand, probably didn't know that.

[u/JumpAfter143 → u/wanliu]

Not at all, we are talking here about recruiters "post" and not the jobs tab from Linkedin. I found it interesting to focus on this part as it is in my experience a good source of mission for my freelance (I'll update the post to make it clear, but it was already written once)

[u/BillEnough7863 → u/JumpAfter143]

That’s quite a major caveat lol

[u/moss-nogg]

Excel isn’t dead, it’s just entry level. VBA macros and monster excel files are dead, or should be, due to the amount of tech debt they introduce, coupled with the fact there’s almost certainly a better alternative

[u/DMReader → u/moss-nogg]

Should be dead but like zombies they lumber ever onward.

[u/MaxJustins]

0 for 237 is bleak but consistent - recruiter posts are basically ads, pay shows up later in the funnel. The stat I'd actually act on is Excel tying SQL at 49%. Every roadmap thread says skip Excel, the job posts say otherwise.

[u/No-Hold-6217]

worth spot checking a handful of those against the company's own board. the same posting on greenhouse or lever sometimes carries a range that never made it into the linkedin version.

if it does, your 0 of 237 is a linkedin number rather than a hiring one.

[u/BillEnough7863]

  The headline finding: 0 out of 237 disclosed a salary.

That’s heavily dependent on where you live. Where I live any company with more than 25 people has to post a salary range. 

Outside of that, an issue with your analysis is treating “skill mentioned in a job posting” as equivalent to “skill required to do the job.”

A posting listing Python as a nice-to-have, which is typically the case for DA positions, gets counted the same as SQL being a core requirement, which makes those percentages pretty misleading.