46 enriched SWE postings in 2026-W44. Share = postings mentioning the technology ÷ that number — postings still awaiting enrichment are excluded from the denominator, so a processing backlog cannot depress every share at once. The chart shows the top 15; the table in section 3 lists the top 30.
| Technology | Share | Change | Postings |
|---|---|---|---|
| jenkins | 23.9% | +23.2pp | 11 |
| github-actions | 26.1% | +22.8pp | 12 |
| git | 28.3% | +10.7pp | 13 |
| docker | 54.3% | +7.6pp | 25 |
| python | 95.7% | +7.1pp | 44 |
| deep-learning | 26.1% | +4.3pp | 12 |
| aws | 43.5% | +0.5pp | 20 |
| pytorch | 39.1% | +0.1pp | 18 |
| Technology | Share | Change | Postings |
|---|---|---|---|
| azure | 23.9% | -21.4pp | 11 |
| sql | 28.3% | -10.3pp | 13 |
| machine-learning | 52.2% | -8.0pp | 24 |
| rag | 47.8% | -6.0pp | 22 |
| llm | 63.0% | -3.3pp | 29 |
| tensorflow | 28.3% | -0.2pp | 13 |
Change is in percentage points of share, not relative percent: a technology going from 1 to 3 postings would otherwise read as +200% and top the board. Boards consider every technology above the bar, not only the 30 the table below shows.
Premium compares the median advertised monthly salary of postings mentioning a technology against the overall median, over the trailing 90 days. Baseline: S$8000, the median of 421 postings advertising a monthly range — medians pin the unit so the figures are comparable. Separately, 100.0% of SWE postings state pay at all (421 of 421, in any unit); the rest hide it, and no figure here describes them. Entry-friendly is computed over the same 90-day window. Premium mixes seniority in (senior roles name more infrastructure); pick an experience band above to compare within one. Entry-friendly = the share of postings mentioning the technology that ask for at most 2 years' experience, or are Intern/Junior roles with no stated requirement. The table lists the top 30 technologies by postings.
| Technology | Kind | Postings | Share | Salary premium | Entry-friendly |
|---|---|---|---|---|---|
| python | language | 44 | 95.7% | +0.0% | 0.0% |
| llm | ai | 29 | 63.0% | +0.0% | 0.0% |
| docker | tool | 25 | 54.3% | -9.4% | 0.0% |
| machine-learning | ai | 24 | 52.2% | +0.0% | 0.0% |
| rag | ai | 22 | 47.8% | +0.0% | 0.0% |
| aws | cloud | 20 | 43.5% | +9.4% | 0.0% |
| pytorch | ai | 18 | 39.1% | -9.4% | 0.0% |
| git | tool | 13 | 28.3% | -6.2% | 0.0% |
| sql | language | 13 | 28.3% | -6.2% | 0.0% |
| tensorflow | ai | 13 | 28.3% | -9.4% | 0.0% |
| deep-learning | ai | 12 | 26.1% | -6.2% | 0.0% |
| github-actions | tool | 12 | 26.1% | +15.6% | 0.0% |
| azure | cloud | 11 | 23.9% | -3.1% | 0.0% |
| jenkins | tool | 11 | 23.9% | —(n=19) | 0.0% |
| computer-vision | ai | 9 | 19.6% | -6.2% | 0.0% |
| java | language | 8 | 17.4% | +25.0% | 0.0% |
| cpp | language | 7 | 15.2% | +100.0% | 0.0% |
| google-cloud | cloud | 7 | 15.2% | -6.2% | 0.0% |
| javascript | language | 7 | 15.2% | -9.4% | 0.0% |
| langchain | ai | 7 | 15.2% | +0.0% | 0.0% |
| openai | ai | 7 | 15.2% | -7.5% | 0.0% |
| kubernetes | tool | 6 | 13.0% | -6.2% | 0.0% |
| generative-ai | ai | 5 | 10.9% | +0.0% | 0.0% |
| nlp | ai | 5 | 10.9% | +25.0% | 0.0% |
| nodejs | language | 5 | 10.9% | -9.4% | 0.0% |
| react | framework | 5 | 10.9% | -9.4% | 0.0% |
| csharp | language | 4 | 8.7% | -9.4% | 0.0% |
| scikit-learn | ai | 4 | 8.7% | -9.4% | 0.0% |
| spark | framework | 4 | 8.7% | +9.4% | 0.0% |
| typescript | language | 4 | 8.7% | +18.8% | 0.0% |
These are MyCareersFuture's own skill tags — the competencies the employer filled in on the form, over the trailing 90 days (2026-08-08 → 2026-11-05) across 421 postings. They are not the technology ranking above: languages and frameworks appear only in the free-text description, which is why this system reads it separately. "Must-have" is the share of postings listing the tag that marked it essential rather than desirable — a tag that is everywhere but rarely essential is table stakes, one that is usually essential is a filter someone is applying.
| Skill | Postings | Share | Marked must-have |
|---|---|---|---|
| Python | 141 | 33.5% | 17.0% |
| Computer Science | 129 | 30.6% | 7.8% |
| AI Agents | 97 | 23.0% | 6.2% |
| Machine Learning | 85 | 20.2% | 20.0% |
| Artificial Intelligence | 82 | 19.5% | 26.8% |
| PyTorch | 77 | 18.3% | 2.6% |
| Ai | 75 | 17.8% | 5.3% |
| TensorFlow | 69 | 16.4% | 0.0% |
| Data Science | 60 | 14.3% | 8.3% |
| Design | 60 | 14.3% | 0.0% |
| LLMs | 50 | 11.9% | 18.0% |
| Azure AI | 47 | 11.2% | 4.3% |
| AWS | 46 | 10.9% | 15.2% |
| Programming | 46 | 10.9% | 0.0% |
| Collaborate With Engineers | 45 | 10.7% | 0.0% |