This post was originally published on this site.
LinkedIn is an interesting company. Since the early 2000s, it’s been the home of everyone’s resume-of-record on the internet. It’s a social network with feeds and followers. It’s a learning hub. And it offers a wide variety of job-hunting and job-filling services.
Also: Want a programming job? Learning any language helps, but only one is essential
In fact, when I last looked at programming language popularity based on job openings, LinkedIn vastly exceeded Dice and Indeed, offering 2.3 million programming jobs compared to 34K on Dice and 56K on Indeed.
On its About page, the company proclaims itself to be “the world’s largest professional network with more than 1 billion members in more than 200 countries and territories worldwide.” Since 2016, LinkedIn has been a subsidiary of Microsoft.
As you might imagine, with so many members, so many job listings, and being a subsidiary of Microsoft, LinkedIn has both the job-related data to crunch and the predilection to do data analysis.
In January, the company released its Jobs on the Rise report, which listed the 25 fastest-growing jobs in the US. In today’s AI-centric world, it’s no surprise that AI engineer and AI consultant filled the top two slots. But the top 10 list also included physical therapist, travel advisor, and security guard, indicating the cross-sectional nature of LinkedIn’s source data.
Today, the company is drilling down past job titles to skills. If you’ve ever been on LinkedIn, you know the company catalogs skills for each individual member — not only those the member chooses but also those suggested by each member’s professional contacts.
Also: LinkedIn’s new AI tool could be your dream job matchmaker
Thankfully, a few of my contacts think I’m skilled at writing, for example. Oddly enough, sarcasm hasn’t been listed as one of my skills on LinkedIn.
In any case, today LinkedIn is releasing its Skills on the Rise report. This report derives its conclusions based on three factors culled from LinkedIn’s vast pool of job-related data: skill acquisition, hiring success, and emerging demand.
Skill acquisition reflects how often LinkedIn members add a given skill to their profiles. Hiring success reflects the skills attributed to members who have been hired in the past year. Emerging skills is a measure of how many new job listings incorporate the skill.
Lies, damned lies, and statistics
All of these factors measure growth rate, which is the percentage increase year-over-year. It’s important to note that the methodology described by LinkedIn for their rankings does not mention weighting the growth rate. This is a concern because unweighted growth rates can skew the interpretation of overall growth.
For example, let’s say that Python programming went from 500,000 to 750,000 listings. That’s a 50% growth rate. Now, let’s say that Fortran (a very old and mostly obsolete programming language) went from 20 listings to 40 listings. That would be a 100% growth rate.
Also: AI roles take top 2 spots on LinkedIn’s list of the 25 fastest-growing jobs in the US
Displayed without weighting, you’d list the 100% growth rate item (Fortran) as much more popular than the 50% growth rate item (Python). But obviously, there’s vastly more demand for Python programmers than Fortran programmers.
Engineering skills on the rise
All this is to say that the job skills I’m about to discuss make some sense, but don’t make major career or educational changes solely based on this one list. Do your research, study what’s going on in your field, and keep reading ZDNET. With that caveat, here are LinkedIn’s 2025 Engineering Skills on the Rise in the US.
Let’s look at each skill in order of growth, from most growth down to least.
Large Language Model (LLM) development and application: This is a perfect example of the unweighted problem. LLM development is undoubtedly growing at a huge rate, but there are still only a few jobs available for it. There aren’t that many companies developing LLMs (compared to all engineering jobs overall), so that limits the total number of jobs available.
Also: Employers want workers with AI skills, but what exactly does that mean?
People management: It’s kind of funny that this is a growth skill since people management and people skills have always been essential. I’m guessing this is showing up because it’s falling under the engineering umbrella, implying that more engineers will also need management skills. Makes total sense for any stage in your career.
Agile problem solving: Here, LinkedIn is making a clear distinction between problem-solving skills and the more agile approach to problem-solving, which involves flexibility, rapid iteration, creativity, situational awareness, and adaptability. Always good skills to have for any engineer.
AI strategy: By this point, almost every business needs to have some AI strategy in place or at least be exploring how AI can help (or hinder) their business goals. Engineers need to have AI awareness, especially as it relates to business goals, workflows, and decision-making.
Azure SQL: Here we’re diving into a very specific resume skill. The listing isn’t for just SQL (which ZDNET’s programming popularity study also found to be in huge demand), but for SQL in the Azure environment. Cloud computing is still a hot topic, even if it has been eclipsed off the buzz radar by AI. And Azure is growing, especially in the enterprise world.
Communications: How wide-reaching can you get in terms of skill description? Yes, of course, communication skill is important. We’re long past the days when an engineer can just grunt, eat pizza, and turn out designs. Engineers need to be involved in the discussion at all levels, and communication skills are mission-critical.
Cloud applications: This skill could be interpreted as the ability to use cloud applications or the ability to create them. I’m guessing that it’s just the familiarity with cloud-based SaaS apps because that’s the kind of general skill attribution we normally see on LinkedIn. So, yeah, you need to be able to use the cloud apps your employer wants you to use.
Technical documentation: Let’s just narrow this down to the ability to write and document your work. More and more employers expect their hires to be somewhat multidisciplinary. That means you not only need to be able to design and code your project, you’ll need to be able to document it.
Scikit-Learn and Matplotlib: These are two widely used Python libraries for AI development. Scikit-Learn is a machine learning library for incorporating predictive models, data crunching, and statistical analysis in your code, while Matplotlib helps create visualizations based on that data. If you’re looking for a coding job, you probably want to know Python, and if you’re looking for an AI coding job, you’ll want some experience with these two libraries.
What does it all mean for your career?
No matter whether you look at growth or weighted growth percentages, there are three career-related conclusions that are quite obvious from this report.
First, AI and cloud expertise are critical. You might not end up working on a large language model at OpenAI, but almost every company is going to be looking at how to incorporate AI into their offerings and/or workflow. And cloud skills remain important as well. So if you’re looking to bone up on anything, make it your AI and cloud chops. A good place to start? Make sure you can code in Python.
Next, so-called soft skills are also essential. These are the basics like people management and communication skills. You’re going to be part of a team, so being able to communicate, lobby for your pet projects, work with others, and guide and inspire other team members continues to be directly on mission.
Also: LinkedIn gets its own suite of video tools as it grows video presence on platform
Finally, we can lump together adaptability and documentation skills. We’re in a world changing at light speed. It’s going to be necessary to adjust dynamically to succeed. Although these two might seem unrelated, being able to document those changes both for efficient knowledge transfer and to be able to go back and reference the whats, whys, and hows of various decisions is critically important.
Personally, in addition to documenting much of my life here online, I’ve always kept lab notes for my projects. That way, I can always go back into my notes to understand what I was thinking, how and what I decided, and to find critical information I left for myself. Those notes have come in clutch more times than I can count, and I’ve always been grateful I took the extra time to write stuff down.
So what’s the big takeaway from this latest research from LinkedIn? I’d say there are three components. First, AI is here to stay. Build your skills there, whether at the prompting level, the coding level, the strategy level, or all of the above. Second, people skills are mission-critical. You have to be able to work and play well with others. And third, you need to be able to document your work and produce clear written communication. If you don’t write much, start practicing now. You’ll thank me for it later.
What do you think about LinkedIn’s list of engineering skills on the rise? Are you actively developing any of these skills, or do you see other technical or soft skills as more critical for the future?
How do you balance staying up to date with AI and cloud expertise while also strengthening leadership and communication abilities? Have you found technical documentation to be an underrated skill in your career? Let us know in the comments below!
Get the morning’s top stories in your inbox each day with our Tech Today newsletter.
You can follow my day-to-day project updates on social media. Be sure to subscribe to my weekly update newsletter, and follow me on Twitter/X at @DavidGewirtz, on Facebook at Facebook.com/DavidGewirtz, on Instagram at Instagram.com/DavidGewirtz, on Bluesky at @DavidGewirtz.com, and on YouTube at YouTube.com/DavidGewirtzTV.