Data Analyst Salary UK 2026: Ranges by Level & Region

Part of our topic guide on Data Analyst Careers.

By James Cotton, Founder of iO-Sphere

In short

The median gross annual salary for a data analyst in the UK is £38,107, according to the ONS Annual Survey of Hours and Earnings (ASHE Table 14, 2025 provisional). Entry-level roles typically start around the 25th percentile (£30,000); the median for mid-level analysts is £38,107; the 75th percentile, where senior roles begin, is £46,152 (ONS ASHE 2025 provisional, SOC 3544). London and financial-services roles push above these benchmarks; public sector and retail roles tend to sit below. Technical skills, particularly SQL, Python, and Power BI, are consistently associated with higher advertised salaries. A funded programme on the job, such as a Level 4 Data Analyst programme under the ST0118 standard, lets you build those skills without sacrificing income during training.

One number on this page is measured rather than advertised, and it anchors everything else: £38,107, the ONS median for employees actually in post as data analysts. Job boards will show you bigger numbers. Those are ceilings employers advertise, skewed toward specialist vacancies, not what the occupation's middle earns. Read every band below with that distinction in mind: ONS figures are floors you can rely on, advertised figures are indicative at best.

The salary reference table

Source for all anchors: ASHE Table 14: Occupation (4-digit SOC 2020), Office for National Statistics, 2025 provisional, published 23 October 2025, gross annual pay, SOC 3544, UK. ONS typically revises provisionals marginally the following year; check the live table for the current 90th percentile figure.

What is a data analyst?

A data analyst collects, cleans, and interprets structured data to help organisations make better decisions, turning raw figures into clear findings that non-technical stakeholders can act on.

Data analyst vs data scientist: what's the difference?

A data analyst works primarily with existing data to answer defined business questions; a data scientist builds predictive models and statistical algorithms, typically requiring a stronger mathematical and programming background. The ONS classifies data analysts under SOC 3544; there is no dedicated ONS occupation code for data scientists, so no separate official median exists for that role. Industry surveys consistently report higher pay for data scientists, reflecting the heavier mathematical demands. If your goal is data science, use an analyst role as a foundation, then pursue a specialist qualification or postgraduate conversion. This page covers the analyst role and its trajectory.

Demand: why the role keeps paying

The structural case rests on employer need for applied data skills outpacing the supply of people who hold them. ONS Labour Force Survey and NOMIS employment data track the size of SOC 3544 over time (search SOC 3544 on ONS NOMIS for the current employed population), and the DfE's annual Employer Skills Survey consistently identifies data and digital skills gaps among the most commonly reported by UK employers across sectors, which underlies continued apprenticeship investment in data routes.

Short-term vacancy counts fluctuate with hiring freezes and macro conditions. The durable signal is that finance, technology, health and retail have all materially expanded their data teams over the past decade, and that expansion has not reversed. Entry-level supply has grown too, which is why demonstrable applied skill at interview matters more than it did five years ago. A credential alone is no longer sufficient differentiation.

What the bands mean in practice

Entry level, around £30,000 and often below outside London. Employers hiring here are buying evidence of applied ability, not credentials. A portfolio project using SQL, or a dashboard built in Power BI or Tableau on data that behaves like the real thing, consistently outperforms a certificate alone in moving an offer up the band.

Mid level, £35,000 to £46,000, anchored on the £38,107 median. At this level you're expected to work with less supervision, own an analysis end to end, and translate findings for a non-technical audience. The middle 50% of all UK data analysts earn between £30,000 and £46,152, so this band is where most of the occupation lives.

Senior, £46,152 upward. Senior analysts owning complex analytical workstreams, mentoring juniors, or working in finance and technology sit above the 75th percentile, clustering toward the 90th in London financial services. Advertised figures for senior roles run higher still, partly because vacancies concentrate in specialist roles and partly because employers post ceiling figures.

Location: the London premium, honestly sized

The national median covers all UK regions, so London's higher pay pulls it upward: the national picture understates typical London pay and overstates many regional markets. ONS publishes regional earnings tables alongside ASHE; for the precise current split, go directly to the dataset. The stable pattern across years: London commands a meaningful premium, reflecting the concentration of financial and technology employers plus cost-of-living weighting. The South East and Scotland's tech centres (Edinburgh, Glasgow) run closer to the national median; the North of England, Wales and Northern Ireland often sit below it, though remote-first roles have compressed the gap.

On remote roles specifically: the London premium hasn't evaporated, but it's no longer automatic. Some London-headquartered employers pay London benchmarks to remote hires; others apply regional pay frameworks that shrink the premium. If you're evaluating a remote role, establish which approach the employer uses before you negotiate. The difference can be substantial.

Sector: where the same job pays differently

Sector-level ONS cross-tabulations at this granularity live in the ASHE tables; the pattern above is the consistent picture from advertised salary data and recruiter benchmarks. If maximising salary is the primary goal, financial services in London is the most reliable route. If stability, pension and mission matter more, the public sector is worth the trade, and the work is often genuinely interesting.

Skills: what actually moves the number

SQL is non-negotiable at every level. Roles requiring advanced SQL (window functions, complex joins, query optimisation) consistently attract higher bands than basic-querying roles. If you're learning one thing first, SQL is the answer: our post on SQL vs Python: which to learn first works through the sequencing.

Python opens a second tier. Analysts who can use pandas, write reusable scripts and automate reporting appear more often in the higher advertised ranges, particularly in engineering-adjacent and product teams. It doesn't replace SQL; the combination beats either alone.

Power BI / Tableau move you from "can produce a report" to "can own the reporting layer", which is what matters for progression into lead and manager roles.

Cloud platform literacy (Snowflake, BigQuery, Databricks) is an emerging differentiator at junior-to-mid level.

Domain knowledge matters more than most training resources admit. An analyst who understands how a bank earns revenue, how a retailer's supply chain works, or what a public-sector commissioner is optimising is considerably more valuable than one who treats data as a purely technical artefact. It's hard to teach in a classroom and builds naturally through embedded, on-the-job practice.

On soft skills: communication, specifically turning a finding into a recommendation a decision-maker can act on, is the single most-cited differentiator in analyst performance reviews. Analysts who reach senior and lead roles almost always get there by bridging analysis and decisions, not by knowing more tools. For current advertised-salary-by-skill data, Reed's annual Salary Guide and Lightcast's job-posting analytics are the most granular public sources.

Does your training route change your salary?

No published, methodologically sound UK study directly compares starting salaries of apprenticeship-trained vs bootcamp-trained vs self-taught analysts in equivalent roles, so treat any claimed percentage uplift for one route with caution. What the routes structurally change is your income during training:

• Work-embedded training (a funded Level 4 programme): employed and earning throughout, no income gap, and you finish with employer-validated applied experience rather than just a certificate. Skills built by doing, in context, transfer more reliably, and interviewers can usually tell.
• Bootcamps: three to six months with reduced or no income for full-time formats (the DWP-funded Skills Bootcamp route, including our own bootcamp programme, mitigates the fee but not the income interruption). Graduates frequently enter at the lower end of the entry band and build from there.
• Self-study: no income cost alongside employment, but slower, lower accountability, and harder to evidence at interview without real project work.

The funded route's advantage is not a higher starting salary. It's that you earn while learning and avoid the savings drawdown intensive routes can require. Whether that becomes a premium over time depends on you, the employer and the sector, not the training format alone. Full comparison: apprenticeship vs bootcamp.

Where the role leads, and what each step pays

Moving into management or engineering exits SOC 3544 into differently classified, typically better-paid occupations. The £38,107 median is a floor reference for the career, not a ceiling.

Getting started, by starting point

Already employed in a role that touches data (reporting, Excel, business administration)? A funded Level 4 programme on the job is likely the most efficient route: skills built in your existing context, employer bears the training cost, and you finish with a qualification plus demonstrated experience. The Advanced Data & AI programme runs against the Data Analyst standard (ST0118 v1.1, approved for delivery) and is designed for exactly this. The how to become a data analyst guide has the step-by-step.

Changing careers from outside data entirely? An intensive programme compresses the transition to three to six months, and you'll need a portfolio of applied work to make entry-level applications competitive. We've trained over 900 learners in data and AI since 2022 through bootcamps and funded qualifications.

Still researching? Read actual entry-level job postings in your target sector and note what's consistently asked for. SQL is almost always there. Python appears frequently. Domain knowledge of the sector is underrated. Build those three and the qualification layer becomes easier to frame.

Who this path is NOT right for

• If you want to build machine-learning models from scratch, an analyst route alone won't get you there: you'd need data science training on top, from providers beyond us.
• If you need full-time income immediately and your employer won't sponsor training, a full-time bootcamp's income gap may undermine the learning. Self-study alongside employment is slower and safer.
• If you're looking at Level 6 or 7 qualifications, those sit outside what iO-Sphere delivers. Our Level 4 is a strong foundation; we'd rather point you at the right destination than overstate our scope.

For career-changers specifically, how career changers succeed in data addresses the common sticking points directly.

Frequently asked questions

What is the average data analyst salary in the UK in 2025?

The median gross annual salary for a data analyst in the UK is £38,107, according to the ONS ASHE Table 14, 2025 provisional, covering SOC 3544. The middle 50% of earners fall between £30,000 (25th percentile) and £46,152 (75th percentile). These are actual earnings in post: advertised salaries on job boards typically run higher.

How much do entry-level data analysts earn in the UK?

Entry-level data analyst salaries typically sit at or below the 25th percentile of the ONS distribution: around £30,000 gross annually for most roles outside London and financial services. Sector, employer size, and your ability to demonstrate applied skills at interview all affect where your first offer lands within that range.

Is a data analyst salary higher in London?

Yes. London roles consistently attract a meaningful premium above equivalent regional roles, reflecting the concentration of finance and technology employers plus cost-of-living weighting. For the current London-specific figure, use the regional breakdowns published alongside the ONS ASHE Table 14 dataset.

Does SQL or Python knowledge increase a data analyst's salary?

Yes, consistently, though the exact premium varies by employer, sector, and level. Advanced SQL commands more than basic querying; Python expands the roles available and is associated with higher bands, particularly in technology and product analytics. The combination is stronger than either alone. See SQL vs Python: which to learn first.

How does a data analyst salary compare to a data scientist salary in the UK?

There is no dedicated ONS occupation code for data scientists, so no official median exists, unlike data analysts under SOC 3544. Industry surveys consistently report data scientists earning more at equivalent experience, with higher entry floors and ceilings. The roles differ in scope: analysts answer business questions with existing data; scientists build predictive models, usually needing stronger mathematical foundations. An analyst role is a reasonable foundation toward data science, but a stepping stone rather than an equivalent.

Which industry pays data analysts the most?

Financial services and insurance consistently top the advertised range, followed closely by technology. Public sector and retail tend to sit at or below the national median. For precise sector figures, the ONS ASHE cross-tabulation tables at the dataset page are the most reliable benchmark.

What qualifications do you need to become a data analyst in the UK?

None are legally required. What matters most at entry level is demonstrable applied skill: can you write SQL, clean a messy dataset, build a chart that tells a story? A quantitative degree can accelerate the path in some sectors but is not a prerequisite. The Level 4 Data Analyst programme under ST0118 is designed for people entering or progressing in data careers without a prior degree.

How much do you earn during a Level 4 Data Analyst apprenticeship?

The apprenticeship minimum wage applies as the legal floor, but most employer-sponsored programmes pay at or above the National Living Wage and many pay considerably more: the salary is set by the employer's pay framework, not the training provider, so clarify it before you enrol. Because you're employed throughout, there's no income gap of the kind full-time bootcamps create.

Is a funded employer programme or a bootcamp better for salary outcomes?

No published study directly compares the two in equivalent roles, so treat any precise "X% uplift" claim with caution. The structural difference is clear: a funded Level 4 programme means earning throughout training in a real business context; a bootcamp typically means a period of reduced or no income. Both can lead to the same role and band. Choose on your employment situation and whether an employer will sponsor you: the apprenticeship vs bootcamp guide covers it in detail.

What roles can a data analyst progress into?

Common UK moves: senior/lead analyst, analytics manager, data engineer (typically higher pay; see the Level 5 Data Engineering programme), BI developer, product analyst, and, with further study, data scientist. Progression into management or engineering exits SOC 3544 into typically better-paid classifications, so the ONS median is a floor for the career, not a ceiling.

Salary data: ASHE Table 14: Occupation (4-digit SOC 2020), 2025 provisional, Office for National Statistics, published 23 October 2025. Figures are gross annual pay for SOC 3544 (data analysts), UK, 2025 provisional.

Exploring a data analytics career or want to understand how a funded Level 4 programme works? The Advanced Data & AI programme page is the right next step.