📈 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
2 261 750
Range midpoint · gross HUF/month
1 999 725 – 2 615 025
n = 5 005
· 12.4% publishes pay
daily ±0
weekly ▼3.2%
monthly ▼2.6%
yearly ·
North America
5 115 791
Range midpoint · gross HUF/month
4 379 730 – 6 072 148
n = 13 861
· 69.7% publishes pay
daily ±0
weekly ±0
monthly ±0
yearly ·
All
4 506 138
Range midpoint · gross HUF/month
3 837 061 – 5 404 623
n = 19 805
· 26.3% publishes pay
daily ±0
weekly ▼0.9%
monthly ±0
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 · All · gross HUF/month
4 887 083
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
4 659 288
n = 19 805
Last 7 days
3 732 227
-19.9%
vs the stock · n = 3 908
Last 30 days
4 111 136
-11.8%
vs the stock · n = 7 828
🪜 By level
The career ladder: how much more the next step pays. Bars show the ratio of medians.
Intern
2 631 127
n=228
Junior
2 153 550
-18%
vs previous
n=161
Medior
3 515 021
+63%
vs previous
n=199
Senior
4 659 288
+33%
vs previous
n=6 427
Lead
4 796 326
+3%
vs previous
n=755
Architect
4 796 326
0%
vs previous
n=478
Principal
6 209 186
+29%
vs previous
n=2 870
| Level | Range midpoint | Difference | Range floors – Range ceilings | Range width | Full field (p10–p90) | daily | weekly | monthly | yearly | sample |
|---|---|---|---|---|---|---|---|---|---|---|
| Principal | 6 006 416 | +29% | 5 207 439 – 7 286 852 | +40% | 4 250 860 – 8 633 386 | ±0 | ±0 | ±0 | · | 2 870 |
| Lead | 4 638 671 | 0% | 4 070 025 – 5 574 701 | +37% | 2 461 200 – 7 400 045 | ±0 | ▼2.8% | ±0 | · | 755 |
| Architect | 4 638 671 | 0% | 3 837 061 – 5 481 515 | +43% | 2 405 823 – 6 618 078 | ±0 | ▼2.8% | ▼4.1% | · | 478 |
| Senior | 4 506 138 | -3% | 3 864 468 – 5 372 433 | +39% | 2 159 420 – 6 625 439 | ±0 | ▼0.4% | ±0 | · | 6 427 |
| Medior | 3 399 483 | -27% | 2 655 136 – 4 170 337 | +57% | 2 224 951 – 5 645 960 | ±0 | ±0 | ▲3.6% | · | 199 |
| manager | 2 783 203 | -40% | 2 522 730 – 3 230 325 | +28% | 1 394 282 – 4 966 677 | ▼9.0% | ▲1.3% | ▼7.7% | · | 33 |
| Intern | 2 544 642 | -45% | 2 244 680 – 2 810 373 | +25% | 511 314 – 4 078 247 | ±0 | ▼2.0% | ▲5.0% | · | 228 |
| Junior | 2 110 967 | -55% | 1 918 530 – 2 467 312 | +29% | 1 193 942 – 4 385 212 | ▲2.8% | ▼6.8% | ▼1.9% | · | 161 |
| mid-level | 2 088 349 | -55% | 1 789 166 – 2 466 682 | +38% | 1 013 536 – 4 578 161 | ▼3.5% | ▼3.8% | ▼3.6% | · | 323 |
| entry-level | 1 200 235 | -74% | 1 114 924 – 1 381 730 | +24% | 523 938 – 2 333 460 | ▼17.1% | ▼17.1% | ▼26.3% | · | 48 |
⚙️ 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 | 5 619 419 | +21% | 4 796 326 – 6 851 894 | +43% | 3 272 054 – 8 982 833 | ±0 | ▲1.0% | ▲1.2% | · | 1 030 |
| Spark | 5 407 365 | +16% | 4 549 657 – 6 577 818 | +45% | 2 420 294 – 7 564 491 | ▲0.5% | ▼0.5% | ▼1.7% | · | 1 043 |
| LLM | 5 301 338 | +14% | 4 475 904 – 6 303 742 | +41% | 2 603 720 – 8 276 539 | ±0 | ▼0.7% | ±0 | · | 3 042 |
| AI/ML | 5 278 808 | +13% | 4 522 250 – 6 303 742 | +39% | 2 684 690 – 8 153 754 | ▲0.8% | ▼0.4% | ▼0.4% | · | 3 945 |
| C/C++ | 5 115 791 | +10% | 4 357 804 – 6 029 667 | +38% | 2 683 174 – 7 605 890 | ±0 | ±0 | ▲0.5% | · | 2 318 |
| Go | 4 983 258 | +7% | 4 177 051 – 5 999 518 | +44% | 2 420 437 – 7 236 970 | ▲0.3% | ▲0.4% | ▲3.6% | · | 921 |
| TypeScript | 4 903 738 | +5% | 4 156 992 – 5 837 813 | +40% | 2 153 550 – 7 537 083 | ±0 | ▼1.2% | ▲1.9% | · | 2 420 |
| Kubernetes | 4 903 738 | +5% | 4 105 655 – 5 837 813 | +42% | 2 122 785 – 7 523 379 | ▲0.4% | ▼0.4% | ▲1.7% | · | 3 171 |
| GCP | 4 893 135 | +5% | 4 111 136 – 5 778 887 | +41% | 2 153 550 – 7 263 007 | ▲1.5% | ▲1.7% | ▲3.7% | · | 2 409 |
| data science | 4 771 205 | +2% | 4 111 136 – 5 780 586 | +41% | 2 074 463 – 7 682 496 | ▲1.4% | ▼2.7% | ▼1.9% | · | 1 657 |
| React | 4 771 205 | +2% | 4 070 025 – 5 574 701 | +37% | 2 028 161 – 7 167 081 | ▲1.0% | ▼0.5% | ±0 | · | 1 971 |
| Python | 4 771 205 | +2% | 4 001 506 – 5 737 228 | +43% | 2 192 718 – 7 476 155 | ▲0.8% | ±0 | ▲1.7% | · | 7 530 |
| AWS | 4 731 444 | +2% | 3 942 580 – 5 645 960 | +43% | 2 167 950 – 7 125 970 | ▲1.4% | ▼0.8% | ▲2.3% | · | 4 115 |
| Terraform | 4 704 938 | +1% | 3 891 876 – 5 591 145 | +44% | 2 119 709 – 7 126 518 | ▲1.4% | ±0 | ▲1.7% | · | 1 667 |
| PostgreSQL | 4 448 108 | -5% | 3 617 904 – 5 426 700 | +50% | 1 890 292 – 6 961 632 | ▲1.1% | ▼1.3% | ±0 | · | 1 100 |
| Java | 4 422 642 | -5% | 3 647 948 – 5 345 848 | +47% | 1 975 000 – 6 906 709 | ▲0.7% | ▼0.4% | ▲0.7% | · | 2 404 |
| Azure | 4 373 604 | -6% | 3 645 207 – 5 345 848 | +47% | 1 864 359 – 6 927 759 | ▲1.1% | ±0 | ▲1.9% | · | 2 579 |
| Linux | 4 176 315 | -10% | 3 542 429 – 5 207 439 | +47% | 1 846 600 – 6 851 894 | ▲0.5% | ▼4.2% | ▲0.6% | · | 2 066 |
| C# | 4 174 804 | -10% | 3 532 836 – 5 144 950 | +46% | 1 845 900 – 7 122 681 | ±0 | ▼3.0% | ▲1.3% | · | 1 047 |
| GitHub | 4 162 876 | -11% | 3 583 540 – 4 933 364 | +38% | 1 999 725 – 6 577 818 | ▲1.7% | ±0 | ▲1.7% | · | 1 386 |
| JavaScript | 4 108 537 | -12% | 3 562 985 – 4 933 364 | +38% | 1 845 900 – 6 509 299 | ▲1.4% | ▼2.4% | ±0 | · | 1 706 |
| SQL | 4 095 284 | -12% | 3 562 985 – 4 922 400 | +38% | 1 789 121 – 6 851 894 | ▲1.3% | ▼1.3% | ▼0.5% | · | 2 980 |
| Docker | 4 050 626 | -13% | 3 425 947 – 4 897 734 | +43% | 1 764 373 – 6 577 818 | ▲0.7% | ▼1.4% | ▲0.8% | · | 1 648 |
| Git | 3 496 895 | -25% | 2 932 611 – 4 160 709 | +42% | 1 538 250 – 5 728 183 | ▲0.5% | ▼2.4% | ▲2.0% | · | 1 217 |
🧩 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 | 6 361 606 | +37% | 5 591 145 – 7 585 046 | +36% | 3 511 330 – 9 291 168 | ▲0.3% | ▼0.7% | ▼0.6% | · | 483 |
| AI / ML Engineer | 5 521 344 | +19% | 4 796 326 – 6 851 894 | +43% | 2 631 127 – 8 222 273 | ▲1.4% | ±0 | ▲1.6% | · | 679 |
| Product Manager | 5 168 805 | +11% | 4 522 250 – 6 166 704 | +36% | 2 954 978 – 7 498 713 | ▲1.0% | ▲0.9% | ▼1.3% | · | 448 |
| Software Engineer | 5 102 538 | +10% | 4 249 599 – 6 029 667 | +42% | 2 153 550 – 7 882 419 | ±0 | ▼0.3% | ±0 | · | 4 702 |
| Solution / Enterprise Architect | 4 903 738 | +5% | 4 111 136 – 5 850 147 | +42% | 2 495 902 – 6 790 166 | ±0 | ±0 | ▼3.7% | · | 400 |
| Data Scientist | 4 903 738 | +5% | 4 111 136 – 5 892 423 | +43% | 2 123 696 – 7 738 803 | ▲1.0% | ▼2.6% | ▼2.2% | · | 398 |
| Project Manager | 4 888 165 | +5% | 4 239 952 – 5 892 629 | +39% | 2 599 643 – 7 269 859 | ▲0.5% | ▼1.4% | ±0 | · | 476 |
| Security Engineer | 4 572 404 | -2% | 3 763 662 – 5 481 515 | +46% | 1 920 671 – 7 228 748 | ▲0.4% | ▼2.5% | ▲1.5% | · | 686 |
| Architect | 4 506 138 | -3% | 3 775 707 – 5 481 515 | +45% | 2 457 089 – 6 865 598 | ▲1.2% | ▼4.4% | · | · | 386 |
| Other | 4 161 551 | -11% | 3 562 985 – 5 013 421 | +41% | 1 773 267 – 7 133 918 | ▲0.3% | ▼3.4% | ▼2.5% | · | 5 946 |
| IT Consultant | 4 031 535 | -13% | 3 425 947 – 4 964 197 | +45% | 1 922 689 – 6 550 410 | ±0 | ▼7.8% | ▼7.3% | · | 991 |
| Data Engineer | 3 909 737 | -16% | 3 261 556 – 4 522 250 | +39% | 1 535 174 – 6 954 672 | ▲2.6% | ±0 | ±0 | · | 436 |
| Fullstack Developer | 3 514 202 | -25% | 2 858 610 – 4 209 804 | +47% | 1 542 642 – 6 287 298 | ▲0.3% | · | ▼0.5% | · | 413 |
| QA / Test Engineer | 3 472 377 | -25% | 3 069 648 – 4 111 136 | +34% | 1 576 635 – 5 340 366 | ▼0.9% | ▲0.8% | · | · | 442 |
🏠 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 | 4 771 205 | +2% | 4 094 692 – 5 591 145 | +37% | 1 835 150 – 7 824 863 | ▲0.9% | ▼5.1% | ▼2.4% | · | 2 430 |
| On-site | 4 506 138 | -3% | 3 754 838 – 5 459 520 | +45% | 1 849 140 – 7 400 045 | ±0 | ▼0.4% | ±0 | · | 12 446 |
| Remote | 4 400 111 | -6% | 3 815 134 – 5 262 254 | +38% | 1 973 345 – 7 194 488 | ▲1.7% | ▲0.6% | ▲1.7% | · | 4 929 |
🔓 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 | 31 | 31 | Open → |
| Anthropic | 9 916 659 | 297 | 329 | Open → |
| Perplexity | 8 548 844 | 47 | 64 | Open → |
| SpaceXAI | 8 480 453 | 70 | 126 | Open → |
| OpenAI | 8 412 062 | 402 | 476 | Open → |
| Twenty | 8 193 212 | 24 | 24 | Open → |
| Wayve | 8 035 913 | 39 | 199 | Open → |
| Hightouch | 7 933 327 | 33 | 40 | Open → |
| Lambda | 7 878 614 | 60 | 62 | Open → |
| G2i | 7 878 614 | 17 | 38 | 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 | 1 867 086 | 2 973 722 | 4 659 288 | 6 029 667 | 7 403 882 | 3 837 061 | 5 404 623 | 26.3% | 19 805 |
| 2026-10-04 | 1 861 283 | 2 945 826 | 4 630 129 | 6 002 259 | 7 400 045 | 3 798 908 | 5 345 848 | 25.4% | 19 463 |
| 2026-10-03 | 1 845 900 | 2 907 293 | 4 659 288 | 6 030 215 | 7 443 508 | 3 824 727 | 5 373 529 | 24.0% | 18 557 |
| 2026-10-02 | 1 835 900 | 2 911 419 | 4 604 104 | 5 978 578 | 7 389 926 | 3 791 615 | 5 362 427 | 24.8% | 17 953 |
| 2026-10-01 | 1 854 381 | 2 999 698 | 4 568 766 | 5 966 270 | 7 374 525 | 3 762 513 | 5 375 018 | 24.7% | 17 661 |
| 2026-09-30 | 1 908 229 | 3 163 724 | 4 638 232 | 5 969 203 | 7 394 283 | 3 845 027 | 5 377 660 | 26.4% | 17 388 |
| 2026-09-29 | 1 911 894 | 3 135 646 | 4 611 066 | 5 968 844 | 7 380 395 | 3 791 022 | 5 377 337 | 26.6% | 17 615 |
| 2026-09-28 | 1 902 378 | 3 107 267 | 4 568 804 | 5 926 231 | 7 327 704 | 3 762 411 | 5 338 947 | 27.0% | 17 934 |
| 2026-09-27 | 1 887 280 | 3 082 944 | 4 566 067 | 5 926 231 | 7 307 683 | 3 762 398 | 5 338 947 | 26.3% | 17 525 |
| 2026-09-26 | 1 877 067 | 3 072 830 | 4 555 523 | 5 926 231 | 7 324 997 | 3 737 369 | 5 338 947 | 26.0% | 17 409 |
| 2026-09-25 | 1 879 014 | 3 086 952 | 4 590 163 | 5 959 159 | 7 368 420 | 3 758 136 | 5 368 612 | 26.1% | 17 475 |
| 2026-09-24 | 1 873 021 | 3 059 427 | 4 549 236 | 5 906 025 | 7 302 720 | 3 724 627 | 5 320 743 | 26.1% | 17 583 |
| 2026-09-23 | 1 868 163 | 3 022 893 | 4 496 215 | 5 835 498 | 7 215 514 | 3 680 149 | 5 257 205 | 26.2% | 17 655 |
| 2026-09-22 | 1 877 401 | 3 027 885 | 4 511 601 | 5 834 987 | 7 214 883 | 3 700 748 | 5 256 745 | 26.4% | 17 769 |
| 2026-09-21 | 1 882 113 | 3 046 263 | 4 542 905 | 5 880 612 | 7 271 297 | 3 729 575 | 5 297 848 | 26.9% | 18 053 |
| 2026-09-20 | 1 882 113 | 3 054 454 | 4 569 394 | 5 880 612 | 7 271 297 | 3 734 983 | 5 297 848 | 26.1% | 17 563 |
| 2026-09-19 | 1 883 771 | 3 055 264 | 4 569 394 | 5 880 612 | 7 271 297 | 3 734 983 | 5 297 848 | 25.9% | 17 386 |
| 2026-09-18 | 1 890 472 | 3 048 445 | 4 544 758 | 5 848 906 | 7 230 955 | 3 714 846 | 5 269 285 | 26.0% | 17 459 |
| 2026-09-17 | 1 882 992 | 3 035 899 | 4 534 017 | 5 844 089 | 7 226 137 | 3 719 311 | 5 264 945 | 26.1% | 17 421 |
| 2026-09-16 | 1 900 127 | 3 049 426 | 4 559 911 | 5 868 407 | 7 256 206 | 3 721 945 | 5 286 853 | 26.2% | 17 616 |
| 2026-09-15 | 1 900 270 | 3 036 246 | 4 546 462 | 5 851 099 | 7 234 804 | 3 710 967 | 5 271 260 | 26.3% | 17 750 |
| 2026-09-14 | 1 886 387 | 3 018 220 | 4 502 440 | 5 816 361 | 7 189 180 | 3 668 080 | 5 239 965 | 26.8% | 18 067 |
| 2026-09-13 | 1 886 387 | 3 012 980 | 4 493 270 | 5 816 361 | 7 165 652 | 3 668 080 | 5 239 965 | 26.3% | 17 733 |
| 2026-09-12 | 1 877 677 | 3 006 430 | 4 479 698 | 5 816 361 | 7 178 752 | 3 688 935 | 5 239 965 | 25.9% | 17 543 |
| 2026-09-11 | 1 869 621 | 2 983 062 | 4 448 425 | 5 789 102 | 7 130 564 | 3 663 409 | 5 233 442 | 26.1% | 17 785 |
| 2026-09-10 | 1 867 353 | 2 967 323 | 4 424 956 | 5 756 281 | 7 092 943 | 3 644 081 | 5 205 830 | 26.2% | 17 860 |
| 2026-09-09 | 1 870 794 | 2 977 032 | 4 439 434 | 5 771 264 | 7 116 151 | 3 656 004 | 5 222 863 | 26.6% | 18 013 |
| 2026-09-08 | 1 866 594 | 2 968 853 | 4 427 236 | 5 753 780 | 7 066 650 | 3 645 959 | 5 208 513 | 26.9% | 18 282 |
| 2026-09-07 | 1 852 805 | 2 943 458 | 4 428 211 | 5 731 212 | 7 058 829 | 3 646 762 | 5 209 660 | 27.0% | 18 407 |
| 2026-09-06 | 1 850 276 | 2 930 434 | 4 428 211 | 5 730 678 | 7 033 041 | 3 646 762 | 5 209 660 | 26.5% | 17 976 |
🔧 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 15 023
EUR 3 196
GBP 572
PLN 449
CAD 298
HUF 47
CZK 35
SEK 34
Stated period
yearly 17 387
monthly 1 760
hourly 642
daily 51
Range type
19 840 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:
19 103 / 19 840
(96.3%)
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 →