90 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 | 33.3% | +14.9pp | 30 |
| python | 68.9% | +8.0pp | 62 |
| sql | 71.1% | +5.6pp | 64 |
| azure | 36.7% | +5.3pp | 33 |
| hadoop | 13.3% | +3.9pp | 12 |
| generative-ai | 14.4% | +3.7pp | 13 |
| kafka | 12.2% | +3.6pp | 11 |
| google-cloud | 20.0% | +3.4pp | 18 |
| postgresql | 13.3% | +1.7pp | 12 |
| docker | 11.1% | +1.4pp | 10 |
Nothing fell this week.
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 784 postings advertising a monthly range — medians pin the unit so the figures are comparable. Separately, 100.0% of SWE postings state pay at all (784 of 784, 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 |
|---|---|---|---|---|---|
| sql | language | 64 | 71.1% | +3.2% | 0.0% |
| python | language | 62 | 68.9% | +3.2% | 0.0% |
| aws | cloud | 43 | 47.8% | +3.2% | 0.0% |
| azure | cloud | 33 | 36.7% | +3.2% | 0.0% |
| spark | framework | 30 | 33.3% | +3.2% | 0.0% |
| machine-learning | ai | 24 | 26.7% | +12.9% | 0.0% |
| google-cloud | cloud | 18 | 20.0% | -3.2% | 0.0% |
| generative-ai | ai | 13 | 14.4% | +16.1% | 0.0% |
| hadoop | tool | 12 | 13.3% | -11.6% | 0.0% |
| postgresql | database | 12 | 13.3% | -11.6% | 0.0% |
| kafka | tool | 11 | 12.2% | +6.5% | 0.0% |
| docker | tool | 10 | 11.1% | -3.2% | 0.0% |
| git | tool | 9 | 10.0% | -3.2% | 0.0% |
| llm | ai | 9 | 10.0% | +9.7% | 0.0% |
| kubernetes | tool | 8 | 8.9% | -3.2% | 0.0% |
| rag | ai | 8 | 8.9% | +29.0% | 0.0% |
| scala | language | 8 | 8.9% | +6.5% | 0.0% |
| nlp | ai | 7 | 7.8% | +17.4% | 0.0% |
| linux | tool | 6 | 6.7% | -3.2% | 0.0% |
| grafana | tool | 5 | 5.6% | -3.2% | 0.0% |
| java | language | 5 | 5.6% | +9.7% | 0.0% |
| mssql | database | 5 | 5.6% | +0.0% | 0.0% |
| openai | ai | 5 | 5.6% | +25.8% | 0.0% |
| shell | language | 5 | 5.6% | +9.7% | 0.0% |
| airflow | tool | 4 | 4.4% | +3.2% | 0.0% |
| jenkins | tool | 4 | 4.4% | —(n=18) | 0.0% |
| langchain | ai | 4 | 4.4% | +29.0% | 0.0% |
| mysql | database | 4 | 4.4% | +3.2% | 0.0% |
| prometheus | tool | 4 | 4.4% | —(n=17) | 0.0% |
| snowflake | database | 4 | 4.4% | -3.2% | 0.0% |
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 784 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 | 194 | 24.7% | 1.5% |
| Python | 178 | 22.7% | 16.3% |
| Data Pipeline | 175 | 22.3% | 12.0% |
| Data Engineering | 166 | 21.2% | 16.9% |
| Data Science | 156 | 19.9% | 8.3% |
| SQL | 154 | 19.6% | 18.2% |
| Data Governance | 127 | 16.2% | 0.8% |
| AWS | 118 | 15.1% | 20.3% |
| Machine Learning | 104 | 13.3% | 4.8% |
| Design | 100 | 12.8% | 2.0% |
| ETL | 93 | 11.9% | 12.9% |
| Data Quality Assurance | 87 | 11.1% | 1.1% |
| Data Infrastructure | 86 | 11.0% | 17.4% |
| Data Analysis | 80 | 10.2% | 16.2% |
| Data Modelling | 76 | 9.7% | 21.1% |