How we calculate salary estimates

We do not have access to live company salary databases, proprietary HR platforms, or real-time job posting data. We want to be upfront about that.

Instead, our salary estimates are built from a combination of:

• Publicly available salary surveys — including aggregated data published by industry associations, professional bodies, and government statistical agencies across Europe (including Eurostat wage data and national statistics institutes).
• Job market benchmarks — salary ranges cited in publicly available job listings and compensation guides for European markets.
• Internal modelling — we apply structured multipliers for location and experience to produce estimates that are consistent and directionally accurate.

We review and update our model periodically to reflect shifts in the market.

Every estimate starts with a base salary for each role — this is the midpoint we've established for a mid-level professional (roughly 4–6 years of experience) working in a typical European market.

From there, we apply two adjustments:

1. Location multiplier. Salaries vary significantly across Europe. London pays roughly 45% above the European average for comparable roles. Madrid pays roughly 18–20% below. We apply a market multiplier per city and country based on observed salary levels.
2. Experience multiplier. Pay increases as you gain experience, but not linearly. Early-career growth is steep; growth slows as you become more senior. We model this as a smooth curve — from roughly 58% of market rate at entry level to around 145–160% at 15+ years of experience.

We also apply a small role × location adjustment (±4%) that reflects real-world variation — for example, software engineers in London command a slightly higher premium relative to other roles than the location multiplier alone would suggest.

The result is a low / median / high range and a percentile estimate for where your salary sits within that range.

Our salary model draws on the following publicly available sources, applied to different geographies and role types:

• Eurostat Labour Cost Survey — EU-wide wage structure survey covering industry-level gross annual earnings across member states. Used to calibrate location multipliers for continental European markets.
• UK ONS Annual Survey of Hours and Earnings (ASHE) — the UK government's primary earnings survey, covering median gross annual pay by occupation and region. Our UK and London estimates are anchored to this data.
• Glassdoor Salary Insights — aggregated self-reported salary data across roles and cities. Used as a directional market signal, particularly for roles with fewer government survey equivalents.
• Indeed Salary Insights — job-posting-derived salary ranges across European markets. Used to cross-check and calibrate role/location medians against live market supply.
• Levels.fyi Compensation Data — community-verified compensation data with the strongest signal for tech roles (software engineering, data science, product management, DevOps) in major European cities. Used specifically for tech role benchmarking.

No single source is used in isolation. Where sources diverge, we apply judgement and weight towards government survey data for baseline figures and community/aggregated data for role-specific signals.

We assign a confidence level — High, Medium, or Lower — to each role and location combination. This reflects how well-covered that combination is by public benchmark data.

• High confidence — mainstream role (e.g. software engineer, product manager) in a major, well-documented market (e.g. London, Berlin, Amsterdam). Strong signal from multiple sources.
• Medium confidence — reasonable coverage, but either the role or location has fewer available references. Estimates are directional and useful for comparison.
• Lower confidence — niche role (e.g. social media manager, content manager) or a broad market category (e.g. "Europe" as a whole) where public benchmarks are sparse. Treat as a rough guide only.

Confidence labels are shown on each salary page and in calculator results. They are not a measure of whether the estimate is wrong — they are a measure of how much external evidence supports it.

We think transparency here matters. There are real limitations to be aware of:

• We don't cover all roles or industries. Our estimates are best for the six roles listed (software engineering, product, marketing, sales, operations, design). Highly specialised roles, finance, legal, or executive positions are not well represented.
• We don't account for company size or stage. A senior engineer at a Series A startup and one at a FAANG company are not the same. Our estimates reflect a broad market average, not any specific company type.
• We don't include equity, bonuses, or benefits. Total compensation can be significantly higher than base salary, especially in tech. Our tool only estimates gross annual base salary.
• Data is not real-time. We update the model periodically, but salaries can shift quickly in fast-moving markets.
• Currency conversions are not live. For UK locations, we display in GBP using a fixed reference rate baked into our model. We don't apply live FX rates.

Despite these limitations, benchmarking your salary is genuinely useful — even with modelled estimates.

Most people have no external reference point for their salary at all. They accepted an offer, received annual increments, and have no idea whether they're at the 30th or 80th percentile for their role. That asymmetry favours employers.

Our tool gives you a directional signal. If our model puts your current salary in the bottom 25% for your role and location, that's a meaningful data point — even if the exact median is off by a few thousand euros. It tells you there's a conversation worth having.

For a more precise view, we recommend combining our estimate with:

• Job listings for similar roles in your location
• Conversations with recruiters who can share live market rates
• Professional network salary discussions
• National salary survey data published by government bodies

Our goal is to give you enough signal to start the conversation — with your manager, a recruiter, or yourself.