33 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 |
|---|---|---|---|
| kafka | 48.5% | +32.4pp | 16 |
| rag | 33.3% | +27.9pp | 11 |
| google-cloud | 42.4% | +27.0pp | 14 |
| snowflake | 42.4% | +26.0pp | 14 |
| git | 39.4% | +25.6pp | 13 |
| jenkins | 30.3% | +25.1pp | 10 |
| aws | 60.6% | +23.0pp | 20 |
| kubernetes | 33.3% | +22.7pp | 11 |
| java | 33.3% | +22.0pp | 11 |
| airflow | 30.3% | +16.6pp | 10 |
| Technology | Share | Change | Postings |
|---|---|---|---|
| sql | 60.6% | -12.4pp | 20 |
| python | 63.6% | -5.0pp | 21 |
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$10350, the median of 277 postings advertising a monthly range — medians pin the unit so the figures are comparable. Separately, 100.0% of SWE postings state pay at all (277 of 277, 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 | 21 | 63.6% | +1.4% | 0.0% |
| aws | cloud | 20 | 60.6% | -4.8% | 0.0% |
| sql | language | 20 | 60.6% | -1.0% | 0.0% |
| spark | framework | 18 | 54.5% | +1.4% | 0.0% |
| kafka | tool | 16 | 48.5% | +1.4% | 0.0% |
| google-cloud | cloud | 14 | 42.4% | -4.8% | 0.0% |
| snowflake | database | 14 | 42.4% | -8.2% | 0.0% |
| azure | cloud | 13 | 39.4% | -1.0% | 0.0% |
| git | tool | 13 | 39.4% | -3.4% | 0.0% |
| java | language | 11 | 33.3% | -3.4% | 0.0% |
| kubernetes | tool | 11 | 33.3% | +11.1% | 0.0% |
| rag | ai | 11 | 33.3% | +15.9% | 0.0% |
| airflow | tool | 10 | 30.3% | +1.4% | 0.0% |
| jenkins | tool | 10 | 30.3% | -3.4% | 0.0% |
| docker | tool | 9 | 27.3% | +11.1% | 0.0% |
| generative-ai | ai | 9 | 27.3% | +18.4% | 0.0% |
| scala | language | 9 | 27.3% | +1.4% | 0.0% |
| terraform | tool | 9 | 27.3% | +1.4% | 0.0% |
| shell | language | 8 | 24.2% | -8.2% | 0.0% |
| machine-learning | ai | 7 | 21.2% | +11.1% | 0.0% |
| nlp | ai | 7 | 21.2% | —(n=18) | 0.0% |
| scikit-learn | ai | 7 | 21.2% | -8.2% | 0.0% |
| elasticsearch | database | 6 | 18.2% | -13.0% | 0.0% |
| flask | framework | 5 | 15.2% | —(n=7) | 0.0% |
| hadoop | tool | 5 | 15.2% | -3.4% | 0.0% |
| react | framework | 5 | 15.2% | —(n=6) | 0.0% |
| xgboost | ai | 5 | 15.2% | —(n=7) | 0.0% |
| kibana | tool | 4 | 12.1% | —(n=11) | 0.0% |
| pytorch | ai | 4 | 12.1% | +32.9% | 0.0% |
| tensorflow | ai | 4 | 12.1% | —(n=18) | 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 277 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 | 66 | 23.8% | 0.0% |
| Data Pipeline | 61 | 22.0% | 6.6% |
| Python | 60 | 21.7% | 21.7% |
| SQL | 54 | 19.5% | 20.4% |
| Data Governance | 45 | 16.2% | 11.1% |
| Data Science | 45 | 16.2% | 22.2% |
| Data Engineering | 42 | 15.2% | 19.0% |
| AWS | 35 | 12.6% | 14.3% |
| Databricks | 35 | 12.6% | 8.6% |
| Data Modelling | 33 | 11.9% | 21.2% |
| ETL | 33 | 11.9% | 21.2% |
| Data Quality Assurance | 29 | 10.5% | 0.0% |
| Design | 29 | 10.5% | 3.4% |
| PySpark | 27 | 9.7% | 25.9% |
| Data Infrastructure | 26 | 9.4% | 0.0% |