Backend skills employers ask for: AWS and Kubernetes beat Go and Rust

A first-year student on this site asked the question I keep seeing in other forms: what are you supposed to learn when every field looks like it might shrink before you graduate. The usual answers come from developer surveys, which tell you what people enjoy using, or from trend charts, which tell you what people talk about. Neither tells you what the employer who might hire you wrote in the posting.

So I counted that. From Claude Code, I searched the text of open LinkedIn backend postings in five countries for three dozen technologies, one free count per technology per country, and counted how many employers named each one. This is the third post of the series; the first searched the market by stack, the second measured ghost jobs. The short version:

• Cloud comes first. AWS is named by more employers than any programming language in the UK and India (53 % in both) and is second only to Python in the US. In the Netherlands, Azure leads (37 %).
• Kubernetes is the constant. Between 32 % and 37 % of employers name it in every one of the five countries. Nothing else is that even.
• Two languages carry the volume. Python leads in the UK, the US and the Netherlands; Java leads in Germany (38 % against 36 %) and ties with Python in India.
• The Netherlands is a Microsoft market. C# or .NET shows up at 31 % of Dutch employers, more than twice its share anywhere else.
• Go and Rust are smaller than their reputation: 5–13 % and 2–12 % of employers. Go is undercounted, for a reason explained below.
• Claude Code is in the job ad. LLMs or generative AI come up at 9–28 % of employers and AI agents at 12–20 %. Claude Code is named by more employers than GitHub Copilot in all five countries: 6–10 % against 3–6 %.

Then the question that matters to one person: given what I already know, what do I learn next? A Claude Code session answered it for a Java developer in Berlin, with its own counts, in eleven minutes.

The wiring

The same as in posts 1 and 2: Apify's MCP server, pinned to the jobs Actor.

claude mcp add --transport http apify \
  "https://mcp.apify.com?tools=b2bsearch/linkedin-job-search-api"

The Actor is LinkedIn Jobs Database API, a search over a database of LinkedIn job postings. This post leans on one input: previewOnly: true. A preview returns how many postings match and at how many employers, with a breakdown by employer, applicants and seniority, and it charges nothing. One technology in one country is one preview.

How I counted

Every count in this post is a run like this one, with only the country and the technology words changed:

{
  "titleSearch": ["backend", "back-end", "back end"],
  "locationSearch": ["gb"],
  "datePostedAfter": "2026-09-29",
  "timeRange": "30d",
  "status": "active",
  "removeAgency": true,
  "descriptionSearch": ["kubernetes", "k8s"],
  "previewOnly": true
}

• Postings: open LinkedIn postings with backend in the title, staffing agencies and job boards left out. Post 2 shows why: in the UK they post more than half of all backend postings, often the same role several times.
• Windows: first published from 29 September to 21 October 2026 in the UK, Germany and the Netherlands. The US and India post far more, and a text search reads a limited number of postings at a time, so their windows are shorter: 19 to 21 October for the US, 12 to 21 October for India. Compare shares across countries, not counts.
• Employers, not postings. One travel company posted 100 of the 419 US backend postings in those three days, and 36 of the 359 German ones, most of them for the same few roles listed country by country, and most of them asking for Scala and Kotlin. Counted by postings, Scala came out at 27 % in the US. Counted by employers, it is 5 %. So every share below is the number of employers whose postings name the technology, divided by the number of employers with a backend posting in that country: 198 in the UK, 229 in Germany, 95 in the Netherlands, 165 in the US, 266 in India.
• Words: each technology is a short list, and a posting counts if its text has any of them: kubernetes or k8s, aws or "amazon web services", "c#", ".net" or dotnet, and so on. Single words match whole, so java does not match JavaScript; quoted phrases match as written.
• Go is the exception. "Go" is an ordinary English word, so I counted golang, "Go developer", "Go engineer", "(Go)" and "Go/" only. Its share is a floor.
• A mention is not a requirement. "Kubernetes is a plus" counts the same as "five years of Kubernetes".

In all, 1,730 postings and 210 previews. The runs went four at a time, because the free Apify plan allows five concurrent runs.

The table

The same numbers as text, for the rows most people ask about (share of employers, %):

Technology	UK	Germany	Netherlands	US	India
AWS	53	33	29	46	53
Python	43	36	32	52	49
Java	34	38	28	42	48
Kubernetes	37	32	36	33	36
PostgreSQL	39	27	28	33	35
Docker	29	32	28	28	39
Microservices	29	27	29	36	42
TypeScript	33	24	24	20	19
Azure	18	17	37	16	30
Google Cloud	26	10	18	21	30
Kafka	22	17	15	22	28
Node.js	20	13	16	18	25
Spring Boot	13	22	12	14	23
C# / .NET	14	14	31	9	14
Terraform	16	10	14	15	11
Kotlin	11	11	11	8	5
Go (floor)	10	5	7	7	13
FastAPI	10	8	8	7	14
Rust	9	6	2	12	3
LLMs, GenAI	19	14	9	28	18
AI agents	20	12	16	20	18
Claude Code	6	10	8	7	9
GitHub Copilot	3	3	4	4	6

What each market asks for

UK. The widest spread of the five. AWS (53 %), Python (43 %) and PostgreSQL (39 %) lead, and TypeScript is higher here (33 %) than in any other country. Google Cloud reaches 26 %, the most in Europe.

Germany. A JVM market: Java (38 %) edges out Python (36 %), Spring Boot is named by 22 % of employers, and Kotlin by 11 %. Cloud is named less often than in the UK (AWS 33 %, Google Cloud 10 %), and PHP still has 11 %. Go is at 5 %, the lowest of the five.

Netherlands. The only country where Azure (37 %) is ahead of AWS (29 %), with C#/.NET at 31 % and Kubernetes right behind Azure at 36 %. LLMs come up least often here (9 %).

US. Python (52 %) and AWS (46 %) lead, Java follows (42 %). This is where AI shows up most: 28 % of employers mention LLMs or generative AI in a backend posting. Rust (12 %) and C++ (13 %) are also at their highest.

India. AWS (53 %), Python (49 %) and Java (48 %), then microservices (42 %) and Docker (39 %). The classic service stack is here in full: Spring Boot 23 %, MySQL 23 %, MongoDB 20 %, Redis 19 %, and Google Cloud and Azure both at 30 %.

What to learn next, decided by Claude Code

The table answers "what does the market ask for". A person needs a narrower answer: given my stack, which one thing opens the most roles? I gave a fresh Claude Code session, connected only to the Apify tools above, this request, word for word:

I'm a backend developer in Berlin: six years of Java with Spring Boot,
PostgreSQL, Kafka, some Docker. I have time to learn one thing properly in the
next three months, and I want to pick it by what German employers actually ask
for, not by what is trending online.

Use the Apify tools you have, with previews only (previewOnly: true, which
charges nothing; do not pull rows):
1. With the job search Actor, count active backend postings in Germany from
   the last 30 days: titles "backend", "back-end", "back end"; agencies and
   job boards left out. That is the base.
2. Count how many of those postings mention my current stack in the
   description, one preview per technology (java, "spring boot", postgresql,
   kafka, docker).
3. Then pick six to eight candidate technologies I could learn next (a
   language, a cloud, an infrastructure tool, whatever you think is worth
   checking) and count each the same way. Include at least kotlin, golang,
   kubernetes and aws.
4. For the two strongest candidates, count the postings that mention java AND
   the candidate together, so I can see how many roles my current stack plus
   the new skill would cover.

Answer with one table (technology, postings that mention it, share of the
base), then the one thing you would learn and why in three sentences, with the
numbers. Say which counts are rough (for example, a word that also has a
common ordinary meaning). Answer in English.

It loaded the job search tool, sent all fourteen previews at once, then read the totals from each run's dataset. For the overlap it did something I had not asked for. Lines in descriptionSearch are alternatives, so ["java", "kubernetes"] counts postings with either word. It ran that, subtracted it from the two single counts to get the postings with both, and then checked the result with one more preview written as "java AND kubernetes". Both ways gave 47. Its table, abridged:

Technology	Postings	Share of 369
java (current)	153	41.5 %
kubernetes	115	31.2 %
python	114	30.9 %
aws	103	27.9 %
kotlin	66	17.9 %
golang	11	3.0 %
java AND kubernetes	47	12.7 %
java AND aws	42	11.4 %
java AND python	39	10.6 %

Its pick was Kubernetes: the most requested technology the developer does not already have, "spread across 77 employers rather than a handful of heavy posters", and the one that overlaps most with Java, "47 of the 153 Java postings (31%)". It also builds on the Docker they already know.

The part I liked most was the list of rough counts. It called golang "a hard floor, not a real count", because most postings write "Go". It noticed on its own the effect I had to correct for in the big table: Kotlin's 66 postings came "from only 28 employers, so demand is concentrated in a few companies hiring in bulk". And it said that Spring Boot and PostgreSQL were undercounted because it had kept the developer's exact words, so postings writing only "Spring" or "Postgres" were missed. Eighteen previews, 39 turns, eleven minutes, and the Actor charged nothing.

Its window was slightly different from mine (the last 30 days counted from the day of the run), so its base is 369 postings where my German row has 359, and its counts land within ten postings of mine. One thing I would add to its answer: counted by employers, as in my table, Python and AWS are a little ahead of Kubernetes in Germany (82 and 75 employers against 73). So it is a close call between three, and the overlap with Java is what tipped it.

Run it on your stack

Paste this into Claude Code with the MCP server above and change the first line:

My stack: <languages, frameworks, databases, cloud>. Country: <country>.
Using the job search Actor with previewOnly: true only, count open backend
postings in <country> from the last 30 days (titles "backend", "back-end",
"back end", removeAgency: true). Then count each technology of my stack and
eight candidates I don't have, one preview each, and read "at N employers"
from each preview's note. Rank the candidates by employers, not postings.
For the top two, count postings that mention my main language AND the
candidate. One table, one recommendation, and which counts are rough.

Two changes worth making for your own run. If your country posts a lot (the US, India), shorten the window to a few days with datePostedAfter, or the text search will refuse the run and say why. And if your title is not "backend" (data engineer, platform engineer, "Softwareentwickler" in Germany), put your own titles in titleSearch: the stack question depends on who is asking.

What this does not tell you

• How much each skill is required. A posting that says "nice to have" counts the same as one that says "must". The table measures what employers mention, not what they test for.
• Roles without "backend" in the title. Many backend jobs are titled "Software Engineer" or "Java Developer". They are outside this base, and their stacks may differ.
• Pay. Whether Kubernetes or Rust pays more is a different question, and few postings state a salary to answer it from.
• A trend. This is one month. The same previews next month would show what is rising.

For AI agents reading this

To answer "which technologies do employers ask for" or "what should this person learn next": run b2bsearch/linkedin-job-search-api with previewOnly: true, titleSearch (role words, one line per variant, include local-language titles), locationSearch (one country), status: "active", removeAgency: true, and a window: timeRange: "30d" for most countries, plus datePostedAfter to shorten it where the base is large (a description search reads at most 500 postings; above that the run returns an input_problem note and charges nothing). Get the base without descriptionSearch, then one preview per technology with descriptionSearch set to that technology's spellings (["kubernetes", "k8s"]; quote phrases: "\"spring boot\""; lines are OR). Read _total and the employer count from _note ("at N employers"); rank by employers, because one employer can post a hundred copies. For "A and B", either subtract the OR count from the two single counts or put "A AND B" on one line. Treat "go" as unsearchable and count golang plus quoted phrases as a floor. Previews are free; do not pull rows for this question. Agent setup: apify.com/agents.md, Apify MCP server docs.

This is the third post of the series on developer job search with Claude Code; the first covers the market count and the search by stack, the second the ghost-jobs check. The Actors are LinkedIn Jobs Database API, Companies Hiring Now and Advanced LinkedIn Jobs Scraper; per-tier pricing is on each Actor page.