Senior Data Analyst

London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Salary: £65,000 - £75,000

Location: Fully Remote

We are currently looking for an Senior Data Analyst to join a fast-growing, innovative, and data-driven tech team within a global cybersecurity education company. You'll play a pivotal role in shaping data strategy and delivering insights that drive smarter decisions across the business.

As an Senior Data Analyst, you'll own the full data journey, from managing pipelines and creating models to developing visualisations that help teams understand user behaviour and business performance. This is a high-impact role, giving you the chance to transform complex data into meaningful stories that influence strategy and product direction.

The Opportunity

As part of a rapidly scaling technology company, you'll work with modern data tools to deliver real-time insights and automation. This Senior Data Analyst role stands out because you'll have genuine ownership of analytics and visibility across the organisation, not just building dashboards, but defining how data drives growth.

Key Responsibilities:

Design, build, and maintain data models and pipelines.

Create engaging dashboards and visualisations to present findings to non-technical audiences.

Collaborate with stakeholders to translate business needs into data-driven outcomes.

Use analytics to uncover trends, opportunities, and risks that shape company strategy.

Champion data best practices and innovation within the wider team.

What's in it for you?

Competitive salary (based on geography and experience).

Fully remote working - work from anywhere in the world.

£2,500 personal development budget for certifications, training, and learning.

Health insurance (where applicable).

Skills and Experience

Must Have:

2+ years' experience as a Data Analyst, Data Engineer, or Analytics Engineer.

dbt
Advanced SQL skills and experience with data visualisation tools (Tableau preferred).

Knowledge of data modelling, warehousing, and analytics best practices.

Strong communication skills with the ability to explain technical findings clearly.

Nice to Have:

Exposure to event-based analytics and user behaviour tracking.

Understanding of machine learning models and techniques.

Experience in a start-up or fast-scaling tech environment.

If you'd like to be considered for this exciting Senior Data Analyst opportunity and think you'd be a great fit, please click the Apply button below to submit your CV. We look forward to hearing from you

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.