150 enriched SWE postings in 2026-W37. 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 |
|---|---|---|---|
| spark | 34.0% | +11.9pp | 51 |
| kubernetes | 13.3% | +8.1pp | 20 |
| rag | 14.0% | +7.8pp | 21 |
| kafka | 18.0% | +7.3pp | 27 |
| jenkins | 9.3% | +6.8pp | 14 |
| google-cloud | 22.7% | +6.0pp | 34 |
| nlp | 10.0% | +5.7pp | 15 |
| docker | 13.3% | +5.2pp | 20 |
| scala | 12.0% | +4.6pp | 18 |
| git | 15.3% | +4.4pp | 23 |
| Technology | Share | Change | Postings |
|---|---|---|---|
| sql | 65.3% | -2.5pp | 98 |
| machine-learning | 26.7% | -1.3pp | 40 |
| llm | 10.0% | -0.5pp | 15 |
| airflow | 9.3% | -0.2pp | 14 |
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$7750, the median of 1330 postings advertising a monthly range — medians pin the unit so the figures are comparable. Separately, 100.0% of SWE postings state pay at all (1330 of 1330, 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 | 98 | 65.3% | +3.2% | 21.5% |
| sql | language | 98 | 65.3% | +0.0% | 19.7% |
| aws | cloud | 69 | 46.0% | +5.2% | 12.7% |
| spark | framework | 51 | 34.0% | +9.7% | 12.9% |
| azure | cloud | 50 | 33.3% | +3.2% | 13.9% |
| machine-learning | ai | 40 | 26.7% | +3.2% | 25.8% |
| google-cloud | cloud | 34 | 22.7% | +5.8% | 14.5% |
| kafka | tool | 27 | 18.0% | +19.4% | 13.0% |
| generative-ai | ai | 23 | 15.3% | +16.1% | 15.6% |
| git | tool | 23 | 15.3% | +9.7% | 12.5% |
| rag | ai | 21 | 14.0% | +29.0% | 15.8% |
| docker | tool | 20 | 13.3% | +12.9% | 14.7% |
| kubernetes | tool | 20 | 13.3% | +22.6% | 16.2% |
| hadoop | tool | 19 | 12.7% | -3.2% | 26.8% |
| scala | language | 18 | 12.0% | +16.1% | 21.0% |
| snowflake | database | 18 | 12.0% | +6.5% | 7.9% |
| java | language | 16 | 10.7% | +19.4% | 22.6% |
| postgresql | database | 16 | 10.7% | -3.2% | 16.0% |
| llm | ai | 15 | 10.0% | +9.7% | 16.9% |
| nlp | ai | 15 | 10.0% | +17.4% | 17.1% |
| airflow | tool | 14 | 9.3% | +16.1% | 11.0% |
| jenkins | tool | 14 | 9.3% | +16.1% | 8.5% |
| shell | language | 14 | 9.3% | +9.7% | 7.6% |
| terraform | tool | 13 | 8.7% | +12.9% | 4.8% |
| scikit-learn | ai | 9 | 6.0% | -7.1% | 38.1% |
| linux | tool | 8 | 5.3% | +3.2% | 10.0% |
| mssql | database | 8 | 5.3% | +3.2% | 14.6% |
| openai | ai | 8 | 5.3% | +22.6% | 16.2% |
| deep-learning | ai | 7 | 4.7% | +6.5% | 34.1% |
| elasticsearch | database | 7 | 4.7% | +25.8% | 22.4% |
These are MyCareersFuture's own skill tags — the competencies the employer filled in on the form, over the trailing 90 days (2026-06-23 → 2026-09-20) across 1330 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 |
|---|---|---|---|
| Computer Science | 330 | 24.8% | 0.9% |
| Python | 329 | 24.7% | 15.5% |
| SQL | 286 | 21.5% | 16.8% |
| Data Pipeline | 273 | 20.5% | 10.3% |
| Data Science | 255 | 19.2% | 9.4% |
| Data Engineering | 253 | 19.0% | 15.8% |
| Machine Learning | 192 | 14.4% | 6.8% |
| Data Governance | 188 | 14.1% | 3.2% |
| AWS | 165 | 12.4% | 19.4% |
| Design | 153 | 11.5% | 2.0% |
| ETL | 152 | 11.4% | 15.8% |
| Data Analysis | 148 | 11.1% | 14.2% |
| Statistics | 147 | 11.1% | 2.7% |
| Data Modelling | 139 | 10.5% | 16.5% |
| Data Quality Assurance | 138 | 10.4% | 0.7% |