📈 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 · Europe · gross HUF/month
2 905 833
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
2 307 375
n = 5 005
Last 7 days
2 153 550
-6.7%
vs the stock · n = 1 469
Last 30 days
2 199 698
-4.7%
vs the stock · n = 2 443
🪜 By level
The career ladder: how much more the next step pays. Bars show the ratio of medians.
Intern
887 693
n=52
Junior
1 483 458
+67%
vs previous
n=68
Senior
2 494 152
+68%
vs previous
n=1 726
Lead
2 738 085
+10%
vs previous
n=171
Architect
2 691 938
-2%
vs previous
n=126
Principal
4 385 513
+63%
vs previous
n=313
| Level | Range midpoint | Difference | Range floors – Range ceilings | Range width | Full field (p10–p90) | daily | weekly | monthly | yearly | sample |
|---|---|---|---|---|---|---|---|---|---|---|
| Principal | 4 385 513 | +90% | 3 645 207 – 5 097 761 | +40% | 2 586 917 – 7 633 010 | ▲2.9% | ▲11.4% | ▲12.2% | · | 313 |
| manager | 3 029 871 | +31% | 2 630 408 – 3 350 770 | +27% | 1 389 353 – 5 018 539 | ±0 | ▲8.1% | ▲1.6% | · | 32 |
| Lead | 2 738 085 | +19% | 2 399 670 – 3 064 194 | +28% | 1 692 075 – 5 885 653 | ▲2.3% | ▲7.1% | ▲0.5% | · | 171 |
| Architect | 2 691 938 | +17% | 2 307 375 – 3 076 500 | +33% | 1 845 900 – 5 393 198 | ±0 | ▼4.4% | ▼2.8% | · | 126 |
| Senior | 2 494 152 | +8% | 2 153 550 – 2 894 323 | +34% | 1 592 625 – 5 207 439 | ▲0.7% | ▼1.8% | ▼0.5% | · | 1 726 |
| mid-level | 2 144 831 | -7% | 1 799 033 – 2 488 553 | +38% | 1 018 809 – 4 617 628 | ±0 | ▼3.8% | ▲1.2% | · | 320 |
| Junior | 1 483 458 | -36% | 1 329 909 – 1 692 075 | +27% | 870 803 – 2 082 175 | ▲0.5% | ▲16.0% | ▼2.0% | · | 68 |
| entry-level | 1 224 447 | -47% | 1 114 924 – 1 381 730 | +24% | 523 938 – 2 333 460 | ▼0.5% | ▼16.2% | ▼25.1% | · | 48 |
| Intern | 887 693 | -62% | 865 543 – 891 201 | +3% | 443 016 – 2 127 264 | ▼0.4% | ▲3.9% | ▲1.5% | · | 52 |
⚙️ 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| AI/ML | 2 922 675 | +27% | 2 461 200 – 3 562 985 | +45% | 1 538 250 – 6 306 208 | ±0 | ▼6.8% | ±0 | · | 613 |
| LLM | 2 809 436 | +22% | 2 361 402 – 3 322 620 | +41% | 1 538 299 – 6 220 971 | ▲1.5% | ▼7.1% | ▼2.3% | · | 555 |
| TypeScript | 2 660 928 | +15% | 2 215 080 – 3 076 500 | +39% | 1 529 513 – 6 015 963 | ±0 | ▲1.1% | ▲3.4% | · | 627 |
| C/C++ | 2 653 481 | +15% | 2 153 550 – 3 027 276 | +41% | 1 655 108 – 6 166 704 | ▲1.5% | ▼5.3% | ▲0.3% | · | 325 |
| AWS | 2 586 312 | +12% | 2 164 721 – 2 953 440 | +36% | 1 545 941 – 5 505 497 | ±0 | ▲2.0% | ▲1.8% | · | 1 026 |
| Python | 2 491 965 | +8% | 2 153 550 – 2 830 404 | +31% | 1 384 425 – 5 618 553 | ▲1.2% | ▲2.3% | ▲2.9% | · | 1 506 |
| React | 2 491 965 | +8% | 2 153 550 – 2 851 253 | +32% | 1 473 951 – 5 686 250 | ▲0.7% | ▼3.9% | ▼3.9% | · | 533 |
| GitHub | 2 477 828 | +7% | 2 104 326 – 2 953 440 | +40% | 1 462 471 – 4 960 771 | ▲0.7% | ▲0.5% | ▼4.1% | · | 447 |
| Terraform | 2 464 002 | +7% | 2 153 550 – 2 873 311 | +33% | 1 621 139 – 5 344 477 | ±0 | ▲1.2% | ▲1.7% | · | 478 |
| Kubernetes | 2 461 200 | +7% | 2 095 163 – 2 873 311 | +37% | 1 615 163 – 5 297 542 | ±0 | ▲1.1% | ▲0.8% | · | 864 |
| GCP | 2 461 200 | +7% | 2 149 920 – 2 799 615 | +30% | 1 569 015 – 5 393 218 | ±0 | ▲1.7% | ▲2.7% | · | 568 |
| PostgreSQL | 2 442 701 | +6% | 2 067 408 – 2 805 768 | +36% | 1 384 425 – 5 049 846 | ±0 | ▲0.3% | ▲0.7% | · | 384 |
| MySQL | 2 412 004 | +5% | 2 153 550 – 2 670 080 | +24% | 1 546 864 – 4 330 534 | ▲0.9% | · | · | · | 238 |
| Node.js | 2 403 570 | +4% | 2 061 255 – 2 768 850 | +34% | 1 379 964 – 5 522 626 | ▼0.9% | ▼1.8% | ▼1.2% | · | 265 |
| data science | 2 323 303 | +1% | 2 025 798 – 2 623 076 | +29% | 1 101 446 – 5 682 961 | ▲0.4% | ▼7.5% | ▼5.7% | · | 344 |
| Java | 2 299 684 | 0% | 1 968 960 – 2 615 025 | +33% | 1 459 738 – 3 930 178 | ▲1.0% | ▲1.1% | ▲2.7% | · | 601 |
| Docker | 2 262 766 | -2% | 1 939 723 – 2 605 180 | +34% | 1 382 641 – 4 316 419 | ▲0.4% | ▼0.9% | ▼0.3% | · | 567 |
| JavaScript | 2 171 647 | -6% | 1 845 900 – 2 466 682 | +34% | 1 315 564 – 5 275 958 | ▲0.8% | ▲1.9% | ▲2.5% | · | 453 |
| Azure | 2 156 273 | -7% | 1 845 900 – 2 513 008 | +36% | 1 356 152 – 4 472 916 | ±0 | ▲3.2% | ▲4.0% | · | 714 |
| C# | 2 153 550 | -7% | 1 845 900 – 2 484 578 | +35% | 1 303 847 – 3 938 434 | ±0 | ▼6.3% | ▼3.8% | · | 266 |
| Linux | 2 153 550 | -7% | 1 815 254 – 2 510 655 | +38% | 1 379 964 – 4 472 916 | ±0 | ±0 | ▲1.1% | · | 520 |
| SQL | 2 117 950 | -8% | 1 845 900 – 2 461 200 | +33% | 1 199 835 – 4 941 586 | ±0 | ±0 | ▲1.6% | · | 790 |
| Git | 1 988 957 | -14% | 1 693 275 – 2 253 022 | +33% | 1 172 916 – 3 700 023 | ±0 | ▲4.5% | ▲5.1% | · | 456 |
| CSS | 1 938 195 | -16% | 1 676 631 – 2 307 375 | +38% | 1 199 835 – 3 875 945 | ±0 | ▲4.3% | ▲5.6% | · | 248 |
🧩 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| Solution / Enterprise Architect | 2 812 217 | +22% | 2 307 375 – 3 563 188 | +54% | 1 724 378 – 5 207 439 | ±0 | ▼3.5% | ▼9.3% | · | 104 |
| AI / ML Engineer | 2 768 850 | +20% | 2 367 367 – 3 182 705 | +34% | 1 536 229 – 5 624 034 | ±0 | ▲1.1% | ▲2.1% | · | 120 |
| Architect | 2 692 553 | +17% | 2 307 375 – 3 076 500 | +33% | 1 981 891 – 5 138 920 | ±0 | ▼8.2% | ▼3.8% | · | 101 |
| Data Scientist | 2 535 697 | +10% | 2 129 250 – 2 771 749 | +30% | 868 127 – 6 109 895 | ▲3.2% | ▼8.3% | ▼2.7% | · | 96 |
| Platform Engineer | 2 522 730 | +9% | 2 163 213 – 2 922 675 | +35% | 1 715 874 – 5 946 073 | ±0 | · | · | · | 95 |
| Security Engineer | 2 458 185 | +7% | 1 999 725 – 2 799 615 | +40% | 1 608 389 – 5 810 680 | ±0 | ▲0.4% | ▼3.3% | · | 209 |
| Software Engineer | 2 419 422 | +5% | 2 061 255 – 2 768 850 | +34% | 1 404 822 – 5 761 757 | ▲0.8% | ▼5.6% | ▼6.0% | · | 988 |
| Backend Developer | 2 389 514 | +4% | 2 138 168 – 2 733 798 | +28% | 1 644 697 – 5 275 958 | ±0 | ▲0.9% | ±0 | · | 154 |
| IT Consultant | 2 307 375 | 0% | 1 924 658 – 2 615 025 | +36% | 1 372 119 – 5 398 757 | ±0 | ▼8.1% | ▼4.7% | · | 247 |
| Fullstack Developer | 2 276 610 | -1% | 1 879 742 – 2 584 260 | +37% | 1 416 841 – 4 796 326 | ▲1.4% | ▼3.5% | ▼3.0% | · | 191 |
| Other | 2 268 919 | -2% | 1 999 725 – 2 605 180 | +30% | 1 181 376 – 5 755 591 | ▲1.7% | ▼5.6% | ±0 | · | 1 362 |
| DevOps / SRE | 2 199 528 | -5% | 1 845 900 – 2 487 350 | +35% | 1 457 030 – 3 775 393 | ±0 | ▲13.8% | ▲14.4% | · | 150 |
| Data Engineer | 2 056 117 | -11% | 1 795 671 – 2 431 682 | +35% | 1 199 835 – 4 248 174 | ▲0.8% | ▲3.9% | ▲4.7% | · | 178 |
| QA / Test Engineer | 1 754 185 | -24% | 1 508 700 – 2 108 395 | +40% | 1 107 540 – 3 083 352 | ▲0.4% | ▲4.9% | ▲3.4% | · | 101 |
🏠 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| Remote | 3 199 560 | +39% | 2 631 127 – 3 691 800 | +40% | 1 477 707 – 6 372 261 | ±0 | ▲1.1% | ▲2.4% | · | 1 994 |
| On-site | 2 092 020 | -9% | 1 827 595 – 2 399 670 | +31% | 1 199 835 – 3 694 492 | ▲0.6% | ±0 | ▲2.4% | · | 2 466 |
| Hybrid | 1 999 725 | -13% | 1 692 075 – 2 307 375 | +36% | 1 401 038 – 2 710 789 | ±0 | ▲6.0% | ▲5.7% | · | 545 |
🔓 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 | |
|---|---|---|---|---|
| Hightouch | 7 933 327 | 30 | 40 | Open → |
| G2i | 7 878 614 | 16 | 38 | Open → |
| Runway Ml | 7 249 420 | 16 | 19 | Open → |
| Epic Games | 6 161 870 | 20 | 82 | Open → |
| Assured | 5 881 605 | 17 | 17 | Open → |
| Vanta | 5 799 536 | 15 | 37 | Open → |
| Novartis | 5 690 110 | 25 | 28 | Open → |
| Samsara | 5 418 941 | 21 | 83 | Open → |
| Cloudflare | 5 350 816 | 32 | 208 | Open → |
| micro1 | 4 464 548 | 31 | 32 | 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 | 1 292 130 | 1 716 687 | 2 307 375 | 3 461 063 | 5 344 477 | 1 999 725 | 2 615 025 | 12.4% | 5 005 |
| 2026-10-04 | 1 292 130 | 1 706 858 | 2 307 375 | 3 420 465 | 5 273 518 | 1 999 657 | 2 615 025 | 12.1% | 4 897 |
| 2026-10-03 | 1 292 130 | 1 700 210 | 2 284 432 | 3 399 533 | 5 273 924 | 1 979 311 | 2 615 025 | 11.7% | 4 836 |
| 2026-10-02 | 1 285 130 | 1 687 508 | 2 258 157 | 3 365 817 | 5 213 471 | 1 974 636 | 2 600 858 | 11.7% | 4 596 |
| 2026-10-01 | 1 281 700 | 1 678 722 | 2 273 492 | 3 403 592 | 5 237 306 | 1 981 769 | 2 593 917 | 11.2% | 4 403 |
| 2026-09-30 | 1 266 942 | 1 712 174 | 2 366 204 | 3 610 951 | 5 444 881 | 2 015 090 | 2 650 764 | 12.1% | 4 081 |
| 2026-09-29 | 1 272 856 | 1 728 429 | 2 370 854 | 3 609 817 | 5 426 358 | 2 019 050 | 2 682 098 | 12.4% | 4 200 |
| 2026-09-28 | 1 260 216 | 1 716 816 | 2 359 100 | 3 576 700 | 5 338 947 | 2 009 040 | 2 669 367 | 12.6% | 4 287 |
| 2026-09-27 | 1 255 912 | 1 711 299 | 2 359 100 | 3 552 822 | 5 338 947 | 2 007 803 | 2 638 904 | 12.3% | 4 219 |
| 2026-09-26 | 1 263 138 | 1 704 640 | 2 351 281 | 3 549 304 | 5 338 947 | 2 005 289 | 2 630 016 | 12.3% | 4 224 |
| 2026-09-25 | 1 266 147 | 1 708 700 | 2 361 767 | 3 548 543 | 5 368 612 | 2 004 960 | 2 644 525 | 12.3% | 4 236 |
| 2026-09-24 | 1 259 836 | 1 703 961 | 2 352 706 | 3 531 819 | 5 320 743 | 1 995 279 | 2 641 103 | 12.4% | 4 267 |
| 2026-09-23 | 1 261 289 | 1 702 439 | 2 335 204 | 3 494 068 | 5 257 205 | 1 988 690 | 2 639 534 | 12.4% | 4 303 |
| 2026-09-22 | 1 261 619 | 1 702 711 | 2 340 500 | 3 500 258 | 5 256 745 | 1 993 200 | 2 645 520 | 12.5% | 4 306 |
| 2026-09-21 | 1 232 097 | 1 699 973 | 2 352 642 | 3 518 626 | 5 310 200 | 2 003 540 | 2 648 871 | 12.7% | 4 362 |
| 2026-09-20 | 1 259 802 | 1 707 236 | 2 352 642 | 3 534 778 | 5 308 695 | 2 003 540 | 2 648 924 | 12.4% | 4 257 |
| 2026-09-19 | 1 259 802 | 1 706 843 | 2 352 642 | 3 516 753 | 5 313 591 | 2 003 540 | 2 641 030 | 12.2% | 4 184 |
| 2026-09-18 | 1 263 267 | 1 706 006 | 2 344 246 | 3 506 387 | 5 311 439 | 1 996 390 | 2 634 643 | 12.2% | 4 199 |
| 2026-09-17 | 1 260 390 | 1 713 656 | 2 353 740 | 3 497 898 | 5 311 471 | 1 992 327 | 2 624 040 | 12.4% | 4 219 |
| 2026-09-16 | 1 279 165 | 1 723 391 | 2 363 944 | 3 524 014 | 5 320 085 | 2 013 165 | 2 652 192 | 12.4% | 4 298 |
| 2026-09-15 | 1 270 724 | 1 720 095 | 2 359 423 | 3 528 129 | 5 271 260 | 2 009 315 | 2 635 630 | 12.5% | 4 340 |
| 2026-09-14 | 1 268 527 | 1 712 915 | 2 353 740 | 3 492 646 | 5 239 965 | 2 004 475 | 2 624 040 | 12.7% | 4 392 |
| 2026-09-13 | 1 275 575 | 1 712 915 | 2 353 740 | 3 492 646 | 5 239 965 | 2 002 180 | 2 624 040 | 12.5% | 4 340 |
| 2026-09-12 | 1 275 447 | 1 712 915 | 2 353 740 | 3 492 646 | 5 239 965 | 1 994 971 | 2 618 900 | 12.5% | 4 334 |
| 2026-09-11 | 1 276 443 | 1 716 681 | 2 355 049 | 3 495 521 | 5 233 442 | 2 000 133 | 2 616 208 | 12.6% | 4 351 |
| 2026-09-10 | 1 273 825 | 1 713 598 | 2 335 346 | 3 487 854 | 5 205 830 | 1 998 844 | 2 602 915 | 12.7% | 4 377 |
| 2026-09-09 | 1 273 825 | 1 713 598 | 2 333 829 | 3 487 854 | 5 222 863 | 1 989 593 | 2 593 144 | 12.9% | 4 405 |
| 2026-09-08 | 1 271 200 | 1 709 045 | 2 324 480 | 3 480 667 | 5 208 513 | 1 985 493 | 2 584 075 | 13.0% | 4 482 |
| 2026-09-07 | 1 271 480 | 1 707 416 | 2 324 992 | 3 481 433 | 5 209 660 | 1 980 241 | 2 583 314 | 13.2% | 4 561 |
| 2026-09-06 | 1 271 480 | 1 695 307 | 2 288 664 | 3 429 131 | 5 144 539 | 1 967 888 | 2 573 233 | 13.1% | 4 482 |
🔧 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
EUR 3 149
USD 1 224
PLN 438
GBP 42
CZK 35
SEK 34
CAD 33
CHF 30
Stated period
yearly 3 576
monthly 1 076
hourly 331
daily 40
Range type
5 023 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:
4 919 / 5 023
(97.9%)
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 →