📈 IT salary movement
What is being advertised right now? We record the salary ranges PUBLISHED in job ads every day and show how they move. Every figure comes with its sample size.
Hungary
1 662 644
Range midpoint · gross HUF/month
1 400 098 – 1 828 454
n = 58
· 7.1% publishes pay
daily ±0
weekly ▼15.8%
monthly ▼15.8%
yearly ·
Europe
2 307 375
Range midpoint · gross HUF/month
1 999 725 – 2 615 025
n = 5 005
· 12.4% publishes pay
daily ±0
weekly ▼2.2%
monthly ▼0.9%
yearly ·
North America
5 289 662
Range midpoint · gross HUF/month
4 379 730 – 6 072 148
n = 13 861
· 69.7% publishes pay
daily ±0
weekly ▲2.7%
monthly ▲5.2%
yearly ·
All
4 659 288
Range midpoint · gross HUF/month
3 837 061 – 5 404 623
n = 19 805
· 26.3% publishes pay
daily ▲0.6%
weekly ▲2.0%
monthly ▲5.2%
yearly ·
Measuring since: 2026-07-26 · Last measured: 2026-10-05 · 72 days recorded · 99.8% of our salary data is not quoted in forint, so the forint figure also moves with the exchange rate. Base rate fixed on: 2026-07-26 · Currency effect since base: +3.4%
⏱️ Measurable time windows
We only report movement when there is something to compare against. For windows not yet available we state the date — we never fill the gap with an estimate.
daily
1 days
✓ available
weekly
7 days
✓ available
monthly
30 days
✓ available
yearly
365 days
available from: 2027-07-26
294 days to go
How the median moved · North America · gross HUF/month
5 297 884
07-2608-0408-1308-2208-3109-0909-1809-2710-05
🎯 Where do you stand?
Enter your gross monthly pay and we show what share of advertised ranges you sit above. The amount never leaves your browser — the maths runs on your device; we neither see nor store it.
ℹ️ Approximate: we compare against the MIDPOINT of advertised ranges, not actually paid salaries. Bonus, equity and benefits are not included.
🌱 Fresh ads vs full stock
Pay levels in newly posted roles move before the long-standing stock — hence a leading indicator. A cross-sectional comparison within today's live stock.
Full live stock
5 289 662
n = 13 861
Last 7 days
4 875 808
-7.8%
vs the stock · n = 2 269
Last 30 days
5 051 901
-4.5%
vs the stock · n = 5 005
🪜 By level
The career ladder: how much more the next step pays. Bars show the ratio of medians.
Intern
3 157 353
n=162
Junior
3 700 023
+17%
vs previous
n=85
Medior
3 545 855
-4%
vs previous
n=183
Senior
5 234 847
+48%
vs previous
n=4 431
Lead
5 344 477
+2%
vs previous
n=520
Architect
5 151 254
-4%
vs previous
n=343
Principal
6 344 854
+23%
vs previous
n=2 443
| Level | Range midpoint | Difference | Range floors – Range ceilings | Range width | Full field (p10–p90) | daily | weekly | monthly | yearly | sample |
|---|---|---|---|---|---|---|---|---|---|---|
| Principal | 6 344 854 | +20% | 5 234 847 – 7 454 860 | +42% | 4 819 348 – 8 731 231 | ▲0.4% | ▲2.2% | ▲3.9% | · | 2 443 |
| Lead | 5 344 477 | +1% | 4 416 046 – 6 187 260 | +40% | 3 783 706 – 7 742 640 | ±0 | ▲2.2% | ▲6.3% | · | 520 |
| Senior | 5 234 847 | -1% | 4 385 212 – 6 029 667 | +38% | 3 562 985 – 6 962 894 | ±0 | ▲2.4% | ▲5.5% | · | 4 431 |
| Architect | 5 151 254 | -3% | 4 165 951 – 6 029 667 | +45% | 3 405 501 – 7 219 703 | ±0 | ▲2.5% | ▲2.4% | · | 343 |
| Junior | 3 700 023 | -30% | 3 288 909 – 3 974 098 | +21% | 1 902 908 – 4 604 473 | ▲1.7% | ▲5.6% | ▲21.9% | · | 85 |
| Medior | 3 545 855 | -33% | 2 740 758 – 4 288 874 | +56% | 2 411 473 – 5 645 960 | ±0 | ▲2.7% | ▲6.4% | · | 183 |
| Intern | 3 157 353 | -40% | 3 086 093 – 3 288 909 | +7% | 1 478 198 – 4 111 136 | ±0 | ▲2.7% | ▲12.2% | · | 162 |
⚙️ By technology
Median per technology against the overall median — where the premium is.
| Technology | Range midpoint | Difference | Range floors – Range ceilings | Range width | Full field (p10–p90) | daily | weekly | monthly | yearly | sample |
|---|---|---|---|---|---|---|---|---|---|---|
| Rust | 6 057 074 | +15% | 4 933 364 – 6 934 116 | +41% | 4 251 737 – 9 181 538 | ±0 | ▲2.2% | ▲4.7% | · | 834 |
| Spark | 5 892 629 | +11% | 4 933 364 – 6 857 595 | +39% | 4 039 877 – 7 687 825 | ±0 | ▲2.7% | ▲5.2% | · | 839 |
| Databricks | 5 819 313 | +10% | 4 933 364 – 6 777 893 | +37% | 3 694 541 – 7 201 395 | ±0 | ▲3.3% | ▲6.3% | · | 694 |
| LLM | 5 810 406 | +10% | 4 811 844 – 6 851 894 | +42% | 3 713 726 – 8 427 829 | ±0 | ▲1.4% | ▲4.4% | · | 2 326 |
| AI/ML | 5 782 998 | +9% | 4 796 326 – 6 769 671 | +41% | 3 768 542 – 8 336 014 | ▲0.5% | ▲1.9% | ▲4.0% | · | 3 150 |
| TypeScript | 5 618 553 | +6% | 4 659 288 – 6 577 818 | +41% | 3 658 418 – 7 770 048 | ±0 | ▲1.5% | ▲4.7% | · | 1 638 |
| Kubernetes | 5 577 442 | +5% | 4 582 547 – 6 577 818 | +44% | 3 817 848 – 7 824 863 | ±0 | ▲2.7% | ▲5.2% | · | 2 167 |
| Snowflake | 5 550 034 | +5% | 4 635 991 – 6 303 742 | +36% | 3 696 734 – 7 707 010 | ±0 | ▲2.7% | ▲7.9% | · | 665 |
| GCP | 5 481 515 | +4% | 4 522 250 – 6 443 521 | +42% | 3 562 985 – 7 645 275 | ±0 | ▲2.7% | ▲6.0% | · | 1 748 |
| data science | 5 481 515 | +4% | 4 522 250 – 6 303 742 | +39% | 3 288 909 – 8 296 355 | ▲0.4% | ▲1.4% | ▲4.4% | · | 1 240 |
| React | 5 440 404 | +3% | 4 522 250 – 6 303 742 | +39% | 3 528 670 – 7 586 417 | ▲0.5% | ▲2.2% | ▲4.4% | · | 1 345 |
| Terraform | 5 429 098 | +3% | 4 385 212 – 6 331 150 | +44% | 3 768 542 – 7 583 676 | ±0 | ▲2.3% | ▲6.9% | · | 1 117 |
| C/C++ | 5 412 996 | +2% | 4 549 657 – 6 440 780 | +42% | 3 562 985 – 7 687 825 | ±0 | ▲4.0% | ▲6.6% | · | 1 944 |
| AWS | 5 403 102 | +2% | 4 385 212 – 6 303 742 | +44% | 3 562 985 – 7 400 045 | ▲0.5% | ▲2.5% | ▲6.4% | · | 2 916 |
| Python | 5 306 107 | 0% | 4 385 212 – 6 166 704 | +41% | 3 409 502 – 7 674 066 | ±0 | ▲1.9% | ▲5.3% | · | 5 676 |
| Java | 5 289 662 | 0% | 4 330 397 – 6 088 593 | +41% | 3 342 307 – 7 217 237 | ±0 | ▲2.7% | ▲5.5% | · | 1 640 |
| Azure | 5 248 551 | -1% | 4 248 174 – 6 057 074 | +43% | 3 342 307 – 7 271 230 | ±0 | ▲2.7% | ▲6.0% | · | 1 795 |
| Docker | 5 138 920 | -3% | 4 119 359 – 5 935 439 | +44% | 3 506 613 – 7 035 525 | ±0 | ▲2.3% | ▲6.4% | · | 989 |
| C# | 5 125 217 | -3% | 4 165 951 – 6 029 667 | +45% | 3 262 269 – 7 527 490 | ±0 | ▲2.3% | ▲6.1% | · | 698 |
| SQL | 4 988 727 | -6% | 4 111 136 – 5 754 316 | +40% | 2 963 581 – 7 144 131 | ±0 | ▲1.0% | ▲3.7% | · | 2 040 |
| Linux | 4 960 771 | -6% | 3 959 024 – 5 755 591 | +45% | 3 131 076 – 7 091 710 | ▲0.6% | ▲1.9% | ▲5.9% | · | 1 476 |
| JavaScript | 4 933 364 | -7% | 4 105 655 – 5 591 145 | +36% | 3 083 352 – 6 783 375 | ±0 | ▲0.5% | ▲4.6% | · | 1 156 |
| GitHub | 4 851 141 | -8% | 4 001 506 – 5 740 599 | +43% | 3 357 428 – 6 869 265 | ▲0.7% | ▲2.7% | ▲5.2% | · | 874 |
| Git | 4 617 902 | -13% | 3 694 541 – 5 426 700 | +47% | 3 101 167 – 6 084 482 | ±0 | ▲2.7% | · | · | 714 |
🧩 By role
Median per role against the overall median.
| Role | Range midpoint | Difference | Range floors – Range ceilings | Range width | Full field (p10–p90) | daily | weekly | monthly | yearly | sample |
|---|---|---|---|---|---|---|---|---|---|---|
| Engineering Manager | 6 851 894 | +30% | 5 920 036 – 7 885 159 | +33% | 5 429 989 – 9 715 985 | ±0 | ▲2.0% | ▲4.9% | · | 383 |
| AI / ML Engineer | 6 166 704 | +17% | 5 207 439 – 7 184 896 | +38% | 4 078 247 – 8 554 199 | ±0 | ▲2.7% | ▲6.2% | · | 530 |
| Data Scientist | 5 755 591 | +9% | 4 796 326 – 6 662 781 | +39% | 3 594 503 – 8 331 903 | ▼1.1% | ▲1.5% | ▲5.2% | · | 281 |
| Product Manager | 5 690 498 | +8% | 4 705 881 – 6 577 818 | +40% | 3 495 315 – 7 523 379 | ▲0.4% | ▼1.2% | ▲1.6% | · | 360 |
| Software Engineer | 5 618 553 | +6% | 4 604 473 – 6 632 633 | +44% | 3 700 023 – 8 336 014 | ±0 | ▲2.2% | ▲4.0% | · | 3 516 |
| Solution / Enterprise Architect | 5 549 486 | +5% | 4 604 473 – 6 632 633 | +44% | 3 768 542 – 7 131 451 | ±0 | ▲2.7% | ▲4.8% | · | 287 |
| Project Manager | 5 289 662 | 0% | 4 522 250 – 6 084 482 | +35% | 3 541 867 – 7 802 937 | ±0 | ▲2.7% | ▲5.2% | · | 393 |
| Security Engineer | 5 262 254 | -1% | 4 308 841 – 6 052 949 | +40% | 3 562 985 – 7 537 083 | ▲1.1% | ▲2.7% | ▲4.7% | · | 456 |
| Data Engineer | 5 193 735 | -2% | 4 111 136 – 6 029 667 | +47% | 3 335 010 – 7 617 935 | ▲1.2% | ▲3.9% | ▲9.3% | · | 238 |
| Architect | 5 097 809 | -4% | 4 072 766 – 6 059 815 | +49% | 3 562 541 – 7 268 489 | ▲0.3% | ▲1.6% | ▲3.0% | · | 279 |
| DevOps / SRE | 5 042 994 | -5% | 3 837 061 – 6 029 667 | +57% | 3 521 873 – 7 380 038 | ▲1.5% | ▲3.4% | ▲10.8% | · | 209 |
| Other | 4 796 326 | -9% | 4 001 506 – 5 506 182 | +38% | 2 740 758 – 7 400 045 | ±0 | ▲2.7% | ▲5.2% | · | 4 303 |
| IT Consultant | 4 700 399 | -11% | 3 837 061 – 5 481 515 | +43% | 2 877 795 – 6 666 893 | ▲0.9% | ▼2.2% | ±0 | · | 685 |
| QA / Test Engineer | 4 034 395 | -24% | 3 472 540 – 4 686 695 | +35% | 2 787 350 – 5 553 871 | ±0 | ▲2.9% | ▲7.2% | · | 323 |
🏠 By work location
Remote or on-site — the difference in advertised ranges.
| By work location | Range midpoint | Difference | Range floors – Range ceilings | Range width | Full field (p10–p90) | daily | weekly | monthly | yearly | sample |
|---|---|---|---|---|---|---|---|---|---|---|
| Hybrid | 5 626 775 | +6% | 4 659 288 – 6 629 892 | +42% | 3 531 630 – 8 222 273 | ±0 | ▲1.6% | ▲5.4% | · | 1 757 |
| Remote | 5 330 773 | +1% | 4 467 435 – 6 029 667 | +35% | 3 192 982 – 7 537 083 | ▲0.5% | ▲3.5% | ▲6.3% | · | 2 734 |
| On-site | 5 241 699 | -1% | 4 193 359 – 6 029 667 | +44% | 3 151 871 – 7 739 187 | ±0 | ▲1.7% | ▲4.3% | · | 9 370 |
🔓 Pay transparency
What share of ads publish a salary range at all. Our most reliable trend indicator: a ratio, so neither exchange rates nor sample mix can move it.
US
71.1%
13 137 / 18 464
SK
66.1%
119 / 180
LT
64.0%
105 / 164
CA
51.3%
724 / 1 410
EE
47.2%
51 / 108
AT
29.3%
228 / 779
EU
25.3%
1 140 / 4 499
IT
25.0%
143 / 572
PL
16.2%
441 / 2 728
IE
16.0%
153 / 959
CZ
13.9%
45 / 323
GB
13.5%
598 / 4 419
DE
11.3%
1 608 / 14 281
NL
11.3%
144 / 1 279
PT
11.1%
139 / 1 254
ES
10.0%
210 / 2 099
CH
9.7%
39 / 403
FR
7.5%
218 / 2 906
HU
7.1%
58 / 821
SE
0.8%
44 / 5 247
| Country | Publishes pay | With pay | Total ads | Range midpoint | daily | weekly | monthly | yearly |
|---|---|---|---|---|---|---|---|---|
| US | 71.1% | 13 137 | 18 464 | 5 129 045 | ▲2.39pp | ▼0.46pp | ▲2.24pp | · |
| SK | 66.1% | 119 | 180 | 1 338 956 | ±0 | ▲0.79pp | ▲3.38pp | · |
| LT | 64.0% | 105 | 164 | 2 026 528 | ▲1.83pp | ▼5.36pp | ▲4.71pp | · |
| CA | 51.3% | 724 | 1 410 | 3 991 908 | ▲0.67pp | ▲1.62pp | ▲6.12pp | · |
| EE | 47.2% | 51 | 108 | 2 098 904 | ±0 | ▼5.84pp | · | · |
| AT | 29.3% | 228 | 779 | 1 702 495 | ▲0.27pp | ▼1.06pp | ▼7.1pp | · |
| EU | 25.3% | 1 140 | 4 499 | 4 506 138 | ▲1.13pp | ▲0.57pp | ▲2.29pp | · |
| IT | 25.0% | 143 | 572 | 1 389 891 | ▲1.23pp | ▼1.86pp | ▼0.82pp | · |
| PL | 16.2% | 441 | 2 728 | 2 012 542 | ▲0.26pp | ▼0.42pp | ▼0.5pp | · |
| IE | 16.0% | 153 | 959 | 3 271 998 | ▲0.29pp | ▼2.46pp | ▼1.66pp | · |
| CZ | 13.9% | 45 | 323 | 1 998 415 | ▲0.41pp | ▲0.72pp | ▲0.82pp | · |
| GB | 13.5% | 598 | 4 419 | 3 598 913 | ▼1.27pp | ▼0.82pp | ▼1.38pp | · |
| DE | 11.3% | 1 608 | 14 281 | 2 110 967 | ±0 | ▼0.91pp | ▼2.0pp | · |
| NL | 11.3% | 144 | 1 279 | 2 110 967 | ▲0.31pp | ▼1.0pp | ▼0.1pp | · |
| PT | 11.1% | 139 | 1 254 | 1 884 792 | ▲0.33pp | ▼1.19pp | ▼1.25pp | · |
| ES | 10.0% | 210 | 2 099 | 2 412 533 | ▲0.06pp | ▼2.21pp | ▼3.39pp | · |
| CH | 9.7% | 39 | 403 | 3 050 680 | ±0 | ▼1.26pp | ▼2.35pp | · |
| FR | 7.5% | 218 | 2 906 | 1 839 557 | ▲0.17pp | ▼0.95pp | ▼0.62pp | · |
| HU | 7.1% | 58 | 821 | 1 662 644 | ±0 | ▲0.41pp | ▲1.39pp | · |
| SE | 0.8% | 44 | 5 247 | 2 555 166 | · | ▲0.06pp | ±0 | · |
⚖️ The EU pay transparency directive must be transposed by mid-2026; it requires the pay range to be disclosed up front in hiring. This table shows, day by day, where practice actually stands.
Barely more than 5% of Hungarian ads currently publish a range — among the lowest of the countries we measure.
🏢 Pay by company
Which company advertises what — employers publishing the most salary ranges.
| Pay by company | Range midpoint | sample | open roles | |
|---|---|---|---|---|
| Polymarket | 10 258 613 | 27 | 31 | Open → |
| Anthropic | 9 916 659 | 261 | 329 | Open → |
| HubSpot | 9 466 648 | 15 | 42 | Open → |
| Mercor | 8 617 235 | 51 | 225 | Open → |
| Perplexity | 8 548 844 | 46 | 64 | Open → |
| SpaceXAI | 8 480 453 | 62 | 126 | Open → |
| OpenAI | 8 412 062 | 395 | 476 | Open → |
| Wayve | 8 035 913 | 35 | 199 | Open → |
| Decagon | 8 001 718 | 54 | 66 | Open → |
| Sierra | 7 796 546 | 35 | 58 | Open → |
📒 Measurement log
Raw figures for every measurement day, dated — so anyone can check what we claim above.
| Date | p10 | p25 | Median | p75 | p90 | Range floors | Range ceilings | Publishes pay | sample |
|---|---|---|---|---|---|---|---|---|---|
| 2026-10-05 | 3 180 375 | 4 111 136 | 5 289 662 | 6 396 928 | 7 764 963 | 4 379 730 | 6 072 148 | 69.7% | 13 861 |
| 2026-10-04 | 3 197 806 | 4 111 136 | 5 289 662 | 6 372 261 | 7 687 825 | 4 379 730 | 6 061 007 | 67.5% | 13 436 |
| 2026-10-03 | 3 272 464 | 4 168 555 | 5 297 884 | 6 476 410 | 7 811 159 | 4 385 212 | 6 166 704 | 63.4% | 12 627 |
| 2026-10-02 | 3 233 706 | 4 119 184 | 5 235 137 | 6 399 705 | 7 732 187 | 4 333 275 | 6 093 668 | 67.9% | 12 335 |
| 2026-10-01 | 3 208 886 | 4 085 014 | 5 188 236 | 6 350 584 | 7 672 839 | 4 300 015 | 6 038 537 | 68.4% | 12 402 |
| 2026-09-30 | 3 210 463 | 4 087 472 | 5 190 786 | 6 353 705 | 7 676 610 | 4 302 128 | 6 049 868 | 68.5% | 12 422 |
| 2026-09-29 | 3 210 270 | 4 081 399 | 5 189 130 | 6 345 257 | 7 676 148 | 4 301 869 | 6 029 339 | 69.0% | 12 525 |
| 2026-09-28 | 3 187 351 | 4 052 261 | 5 152 084 | 6 283 940 | 7 621 346 | 4 271 157 | 5 969 503 | 70.1% | 12 756 |
| 2026-09-27 | 3 187 351 | 4 052 261 | 5 152 084 | 6 273 262 | 7 607 999 | 4 271 157 | 5 968 942 | 68.2% | 12 438 |
| 2026-09-26 | 3 187 351 | 4 052 261 | 5 152 084 | 6 274 177 | 7 607 999 | 4 271 157 | 5 976 897 | 67.6% | 12 324 |
| 2026-09-25 | 3 205 061 | 4 080 145 | 5 180 710 | 6 341 673 | 7 663 693 | 4 294 889 | 6 039 688 | 67.9% | 12 368 |
| 2026-09-24 | 3 176 484 | 4 043 765 | 5 134 517 | 6 285 128 | 7 595 361 | 4 256 595 | 5 985 836 | 68.0% | 12 444 |
| 2026-09-23 | 3 138 551 | 3 990 219 | 5 074 517 | 6 216 645 | 7 504 660 | 4 205 764 | 5 914 356 | 68.3% | 12 491 |
| 2026-09-22 | 3 138 277 | 3 993 812 | 5 085 901 | 6 236 142 | 7 504 003 | 4 205 396 | 5 913 838 | 69.0% | 12 583 |
| 2026-09-21 | 3 162 815 | 4 021 067 | 5 120 370 | 6 272 652 | 7 549 434 | 4 238 279 | 5 960 079 | 70.0% | 12 793 |
| 2026-09-20 | 3 169 244 | 4 037 258 | 5 125 668 | 6 260 070 | 7 549 434 | 4 238 279 | 5 960 079 | 68.1% | 12 463 |
| 2026-09-19 | 3 164 372 | 4 026 471 | 5 118 384 | 6 259 408 | 7 549 434 | 4 238 279 | 5 960 079 | 67.8% | 12 366 |
| 2026-09-18 | 3 149 188 | 4 005 361 | 5 086 177 | 6 217 756 | 7 508 731 | 4 215 428 | 5 927 946 | 67.9% | 12 418 |
| 2026-09-17 | 3 143 172 | 4 001 358 | 5 080 672 | 6 245 870 | 7 502 547 | 4 211 956 | 5 923 063 | 67.8% | 12 351 |
| 2026-09-16 | 3 156 251 | 4 018 009 | 5 103 135 | 6 246 417 | 7 533 766 | 4 229 483 | 5 947 710 | 68.5% | 12 468 |
| 2026-09-15 | 3 146 942 | 4 006 158 | 5 088 084 | 6 233 265 | 7 511 546 | 4 217 008 | 5 930 168 | 68.9% | 12 559 |
| 2026-09-14 | 3 128 259 | 3 977 133 | 5 056 566 | 6 191 019 | 7 466 950 | 4 191 972 | 5 894 961 | 69.9% | 12 783 |
| 2026-09-13 | 3 131 665 | 3 982 400 | 5 056 566 | 6 192 840 | 7 466 950 | 4 191 972 | 5 894 961 | 68.2% | 12 514 |
| 2026-09-12 | 3 128 259 | 3 982 373 | 5 056 566 | 6 216 236 | 7 466 950 | 4 191 972 | 5 894 961 | 67.2% | 12 338 |
| 2026-09-11 | 3 124 365 | 3 972 182 | 5 050 271 | 6 208 497 | 7 457 654 | 4 186 753 | 5 887 622 | 68.0% | 12 353 |
| 2026-09-10 | 3 107 881 | 3 956 431 | 5 023 626 | 6 175 741 | 7 418 308 | 4 164 664 | 5 856 559 | 68.2% | 12 400 |
| 2026-09-09 | 3 118 049 | 3 964 153 | 5 040 063 | 6 176 966 | 7 442 580 | 4 178 291 | 5 845 066 | 68.9% | 12 506 |
| 2026-09-08 | 3 109 482 | 3 953 262 | 5 026 215 | 6 146 046 | 7 416 011 | 4 166 811 | 5 820 514 | 69.8% | 12 685 |
| 2026-09-07 | 3 110 167 | 3 954 132 | 5 027 322 | 6 135 690 | 7 411 262 | 4 167 728 | 5 808 771 | 69.9% | 12 729 |
| 2026-09-06 | 3 113 084 | 3 954 132 | 5 027 322 | 6 121 351 | 7 400 322 | 4 167 728 | 5 808 771 | 68.1% | 12 405 |
🔧 Exactly how we calculate
Every step from the raw ad to the figures above. If you would compute it differently, the raw snapshot is downloadable — redo the maths.
The formula
érték = (salary_min + salary_max) / 2 ← sáv-közép × periódus_szorzó ← yearly ÷12 · monthly ×1 · daily ×21 · hourly ×160 × árfolyam[currency] ← EKB középárfolyam, HUF-ra medián = percentile_cont(0.5) WITHIN GROUP (ORDER BY érték) transzp = n / total_n ← bért kiíró / összes aktív hirdetés
One ad yields one value. We take the range midpoint, convert the period to monthly, then to forint — in that order.
What the sample consists of
Currency
USD 13 582
CAD 259
PLN 10
GBP 9
EUR 6
INR 5
BRL 3
SGD 2
Stated period
yearly 13 069
monthly 563
hourly 243
daily 1
Range type
13 876 two-sided range (min–max) ·
0 floor only (“from”)
Freshness
We re-check ads at their source daily. Verified live within the last 2 days:
13 256 / 13 876
(95.5%)
Exchange rates
Today (ECB reference) · 2026-10-05
EUR 367.8 USD 328.3 GBP 434.1 PLN 84.0
Fixed base · 2026-07-26
EUR 361.9 USD 318.1 GBP 423.8 PLN 83.9
Update schedule
Daily snapshot at 06:55 (after ad imports and location normalisation).
Raw data
The full daily snapshot — every breakdown and segment, including the rates used:
⬇ /salaries/data.jsonFree to use with attribution (CC BY 4.0).
What we exclude, and why
- ✕ Ads without pay — we do not estimate. They only appear in the transparency denominator.
- ✕ Pay without a period — we never guess yearly vs monthly. The ad then stays without pay.
- ✕ Plausibility filter: ranges that cannot be interpreted are skipped at import rather than transformed.
- ✕ Segments below 15 ads are not shown — both for noise and identifiability.
- ✕ Outliers are NOT trimmed or smoothed. Whatever passes the filter stays — p10–p90 shows the spread.
Definitions
- ›Median: PostgreSQL
percentile_cont(0.5)with linear interpolation — the true interpolated value, not the nearest item. - ›Range midpoint:
(min + max) / 2. Where only a floor exists, it is used for both ends. - ›median_fix: the same, but at the rate fixed on the base day — only this is valid for time-series comparison.
- ›Transparency rate:
n / total_n, where the denominator is all active ads in the segment, with and without pay.
⚠️ What this figure does NOT tell you
- •It measures advertised, not paid, salary. The two can differ in both directions.
- •It excludes bonus, equity, benefits and 13th month — ads rarely quantify these.
- •Not representative of the whole labour market: it measures what is advertised through our sources.
- •Figures are gross, and tax systems differ by country — net comparison needs a separate calculator.
🔬 How we measure
- • The headline figure is the MIDPOINT of the range — neither floor nor ceiling. Both ends are reported separately.
- • Since 99.8% of the data is quoted in foreign currency, every movement is reported twice: at a fixed rate (real market movement) and at the daily rate (forint value). The difference is the currency effect.
- • We only count what the employer published in the ad. We do not ask visitors for their salary, and we do not estimate.
- • Ranges in different currencies and periods (year/month/day/hour) are converted to gross HUF/month at the daily reference rate. We never guess the period: if the source does not state it, the ad stays without salary.
- • A segment appears only with at least 15 ads behind it. Regions are reported separately, because a single blended average would hide the differences.
- • Movement is computed from our own daily snapshots, not from ad posting dates — so the timing of our imports cannot distort the trend.
- • p10–p90 marks the bottom and top tenth of the field, p25–p75 the middle half. Outliers are neither trimmed nor smoothed — the distribution is shown as it is.
An indicative market picture of advertised salary ranges; not advice, and not a survey of actually paid salaries.
Detailed salary statistics →