Turning data into decisions.
Every good business decision starts with understanding what the data is really saying, and experienced data professionals are the people who make that possible, turning raw information into insight on customer trends, risk and strategy. As UK organisations invest in data platforms, analytics and AI, we connect data professionals with start-ups building their first foundations, scale-ups driving growth after product-market fit, and fintechs and regulated organisations where accurate, well-governed data is essential.
Latest roles
West Yorkshire, United Kingdom
£475
Posted 1 week ago
London, United Kingdom
£70,000 - £75,000
Posted 4 weeks ago
London, United Kingdom
£70,000 - £85,000
Posted 2 months ago
Building your career in Data Analytics
SQL and a solid grasp of statistics are the foundation for most data roles. Beyond that, the skills that matter depend on your direction, but these appear consistently across UK data roles:
- SQL: querying and shaping data in relational databases
- BI and visualisation tools: Power BI in particular, alongside Tableau and Looker, to turn numbers into something stakeholders can use
- Excel: still widely used for analysis and quick modelling
- Python or R: for analysis, automation and, increasingly, machine learning workflows
- Statistics and experimentation: A/B testing, hypothesis testing and knowing when a result is meaningful
- Data modelling and cloud platforms: dbt, and platforms such as Snowflake, Databricks, BigQuery or Azure, increasingly expected beyond entry level
- Data quality and governance: understanding data accuracy, lineage and UK GDPR, particularly in financial services and the public sector
- Communication: translating analysis into a decision non-technical colleagues can act on
As AI takes on more routine analysis, commercial awareness is becoming increasingly important. Being able to explain why a number matters, not just how you produced it, is often what sets candidates apart.
A degree in maths, statistics, economics or computing is common, but apprenticeships and moves from other analytical roles are also strong routes. Certifications such as Microsoft’s Power BI credentials, or data certifications from Snowflake, AWS, Azure or Google Cloud, can add structure to your learning and show commitment. What usually makes the strongest case, though, is evidence of what you can do. A portfolio built on public datasets or your own projects lets you show the questions you explored, the methods you chose and what your analysis revealed. If your strongest work is confidential, describing the approach and outcome without sharing the data itself is a good alternative.
Data pay in the UK grows with experience, specialism and scope. Analyst roles build the foundations. As professionals move into analytics engineering, data engineering, data science or leadership, earning potential increases. Experience with cloud data platforms and data modelling, the ability to connect analysis to commercial outcomes, and work in regulated environments such as financial services can all strengthen your position.
| Seniority Level | Typical Permanent Salary |
| Junior Data Analyst (0-2 years) | £26,000 - £36,000 |
| Data Analyst / Mid-level (2-5 years) | £36,000 - £50,000 |
| Senior Data Analyst / Analytics Engineer | £50,000 - £68,000 |
| Data Scientist |
£50,000 - £80,000 |
| Data Engineer |
£50,000 - £85,000 |
| Analytics Manager / Lead Data Scientist |
£70,000 - £95,000 |
| Head of Data / Director of Analytics |
£95,000 - £140,000+ |
Contract data analytics day rates typically range from £300 to £700+, with data engineers and data scientists sitting at the top of that range - specialist MLOps and machine learning engineering contracts in London can push beyond £850. Day rate depends heavily on seniority, platform experience and IR35 status.
Data analyst pay is relatively even across the UK. London offers competitive salaries, but the gap with the rest of the country is smaller than in many technology disciplines, and strong opportunities exist across the North, the Midlands and beyond. Data roles span a wide range of organisations nationwide, and home-based and hybrid working are available in many roles, widening your options further.
| Seniority Level | Average Permanent Salary |
| London | £40,000 - £95,000 |
| South-East | £38,000- £80,000 |
| Manchester & North-West | £34,000- £70,000 |
| Leeds, Birmingham & regional cities |
£32,000- £62,000 |
| Remote (UK-based) | £36,000- £75,000 |
Both careers use data to help organisations make better decisions, but they approach it differently. Data analysts focus on understanding what has happened and why, through analysis, reporting and dashboards that answer business questions. Data scientists focus more on predicting what might happen next, using statistics, experimentation and machine learning to build models and solve more complex problems.
Which suits you depends on how you like to work:
- Data analyst: suits people who enjoy working closely with the business, answering questions quickly and turning findings into clear, practical recommendations
- Data scientist: suits people drawn to deeper statistical work, experimentation and building models, often with stronger programming and mathematical foundations
Many data scientists start as analysts, and the two roles share core skills such as SQL, statistics and clear communication. Starting as an analyst can be a strong way to build business understanding before moving into data science, and both paths offer rewarding long-term careers.
Advancing your data career
AI is changing how data work gets done rather than removing the need for data professionals. Our 2026 Big Tech Employment Survey found that 82% of Data & Analytics respondents say AI has made them more efficient, and many experienced analysts now use AI to clean data, write queries and draft analysis far more quickly. That frees time for the work that needs judgement: framing the right question, checking that results make sense and turning findings into decisions.
In our experience, what data teams are asked to build is changing too. More roles now involve AI-powered analytics features, real-time data feeding machine learning models and the governance that keeps AI outputs reliable. In regulated environments such as financial services, being able to explain and stand behind AI-assisted analysis is especially valued. Experienced professionals who combine strong analytical foundations with practical AI fluency are well placed for the roles emerging around it.
every kind: start-ups building their first data foundations, scale-ups using data to drive growth, and established businesses, financial services firms and public bodies modernising how they use data.
The strongest opportunities tend to go to experienced professionals who combine analytical depth with cloud data platforms, data modelling and AI fluency, and who can show how their work changed a decision or outcome. If your experience has centred on reporting, adding cloud or modelling skills can open a wider range of senior roles. Being clear about where your strengths lie will help you target the opportunities where you’ll stand out.
Data sits at the heart of almost every organisation, so experienced professionals can choose very different environments:
- Start-ups building from 0-1: setting up first data foundations, metrics and dashboards, often owning the whole data stack
- Scale-ups after product-market fit: building reliable pipelines, experimentation and product and customer analytics as the business grows
- Banks, fintechs, insurers and investment firms: risk, fraud, pricing and customer analytics, where accuracy and governance are essential
- Finance and FP&A teams: forecasting, budgeting and performance analysis inside finance functions across many industries
- Public sector and third sector: modernising data capability to improve services, often alongside long-standing systems and strong governance requirements
Many of the most rewarding roles sit in one of two places: established organisations modernising their reporting with cloud platforms and machine learning, or growth-stage businesses building an analytics function from scratch. We can help you understand how teams differ in data maturity, pace and scope.
Experienced data professionals can progress in several directions, depending on whether they want to deepen technical expertise, specialise or lead.
- Lead Data Analyst: owning a data domain, setting standards and guiding how the organisation measures performance
- Analytics or Data Engineering: moving towards data modelling, pipelines and the platforms behind analysis
- Data Science: applying statistics, experimentation and machine learning to more complex problems
- Principal or Staff roles: deepening technical influence across teams without line management
- Analytics Manager or Head of Data: leading teams and shaping data strategy, with the emphasis shifting from hands-on analysis to developing people
The step into senior roles often depends on framing business questions, choosing the right approach and influencing decisions, as well as technical skill. Adding cloud platform, machine learning or AI experience to a strong analytics foundation can broaden the senior roles open to you. We can help you understand where your experience is most valued.
For many experienced analysts, analytics engineering or data engineering is a natural next step, especially if you enjoy building the foundations that make analysis possible. Analytics engineers sit between analysis and engineering, modelling and transforming data so it’s reliable and easy to use. Data engineers focus on the pipelines, platforms and infrastructure that move and store data at scale.
The move usually builds on strong SQL and adds:
- Data modelling: designing clean, well-structured datasets, often with tools such as dbt
- Cloud data platforms: working with Snowflake, Databricks, BigQuery or Azure
- Python and orchestration: automating and scheduling data workflows
- Software engineering practices: version control, testing and CI/CD
Your analytical background is a real asset here, because you understand how data is used. We can help you identify roles that build on your experience as you make the move.
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