Our methodology
TeamCalc benchmarks synthesise institutional statistics, market intelligence, and live platform signals into a single defensible figure for every role. Every number carries a transparent quality rating so you always know how much weight to give it.
We synthesise data from four independent source categories, each contributing a different lens on the UK tech compensation landscape. No single source tells the full story — combining them produces benchmarks that are more robust than any one in isolation.
Our foundation layer. We anchor to official UK labour market statistics published by the Office for National Statistics (ONS), covering hundreds of thousands of employee records. This gives us statistically robust baselines broken down by occupation and region — employer-reported data that eliminates the self-reporting bias inherent in online platforms.
Real-time market signal from the platforms where tech professionals research and report compensation. We aggregate across multiple major salary platforms — including leading job boards, compensation databases, and professional networks. No single platform gives the full picture, which is why we cross-reference across several.
Annual salary surveys and reports from specialist technology recruitment firms and industry bodies, based on actual placements and market analysis. Combined with annual developer surveys, these are particularly valuable for trend detection and seniority calibration — they capture shifts before they appear in the institutional data.
UK-specific tech compensation communities and crowdsourced data. We use this as a validation layer — sense-checking the institutional and platform data against what practitioners report in specialised forums. Community data surfaces outliers and emerging trends, but carries less weight in the final blend than institutional sources.
Institutional data
Government statistics
Market platforms
Compensation databases
Industry research
Recruitment surveys
Community data
Validation layer
TeamCalc Engine
Validate · Weight · Blend
Four independent source categories flow into a single blending engine. Institutional data forms the foundation; market platforms provide real-time signal.
Raw data from four source categories passes through a structured pipeline that validates, weights, and blends it into a single defensible figure for each role, region, and seniority combination.
Every benchmark is derived from multiple independent data points. Institutional data carries more weight than crowdsourced data — as you'd expect from employer-reported statistics covering hundreds of thousands of records. We account for known biases in each source type: some platforms over-represent senior roles at top-tier companies, while others skew toward earlier-career professionals.
The tech salary market moves fast. A three-year-old data point for an ML engineer may bear little resemblance to today's market. More recent data carries proportionally more weight in the blend, and data beyond a defined age threshold is excluded entirely.
We model salary variation across 13 UK regions, anchored to official regional pay data. Some regions have deeper data coverage than others — London and the South East naturally have more data points than Northern Ireland or Wales. We're transparent about this: every benchmark carries a quality score that reflects the depth of its regional data.
We model the full career arc from junior to executive, with distinct benchmarks at each level. Leadership roles — Engineering Manager, VP Engineering, CTO — additionally account for company stage. A CTO at a seed startup operates in a fundamentally different compensation market than a CTO at a public company, and our benchmarks reflect that.
Benchmarks are refreshed as new data becomes available — major updates when institutional data publishes, and incremental updates as new market intelligence surfaces. Aggregate, anonymised usage patterns help us identify where our benchmarks may need adjustment, creating a feedback loop that improves accuracy over time.
Sources
4 tiers
Validation
Outlier removal
Weighting
Recency + bias
Blending
Multi-source
Quality scoring
4 dimensions
Your benchmark
p25 / median / p75
Sources
4 tiers
Validation
Outlier removal
Weighting
Recency + bias
Blending
Multi-source
Quality scoring
4 dimensions
Your benchmark
p25 / median / p75
Our methodology is designed to surface the most defensible figure, not the most optimistic one.
Most salary tools give you a number and leave you to guess how reliable it is. We think you deserve to know. Every TeamCalc benchmark is assigned a quality score based on four dimensions.
How many independent sources contributed to this specific benchmark. More sources mean more confidence.
How closely those sources agree with each other. Tight clustering suggests the market rate is well-established.
How recent the underlying data is. Fresher data receives a higher quality rating.
Whether the benchmark comes from direct data for this exact role/region/seniority, or is estimated from related data.
Live example from the product
Senior Software Engineer · London
p25 – median – p75
Data quality
StrongHover the quality badge to see the full breakdown. Every benchmark in TeamCalc includes this rating, so you always know the confidence level behind the numbers you're using.
Transparency means being honest about limitations, not just capabilities.
If we don't have strong data for a role/region/seniority combination, we say so. We'd rather show an “Indicative” quality rating than present a guess as fact.
Stock options, RSUs, and bonuses vary too widely by company to benchmark reliably. Our figures represent base salary. For early-stage leadership roles where equity is the dominant compensation component, keep this in mind.
Salary data is inherently imprecise — no methodology eliminates uncertainty entirely. Our goal is to give you the most defensible starting point available, with the transparency to adjust based on your own market knowledge.
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