Data Analyst

Match Digital
London
2 days ago
Create job alert

Data Analyst (1 Year Maternity Cover)

£35,000 - £40,000 + 10% bonus + benefits

London (2 days per week in the office)


Our client


Our client is a global strategic technology and payments partner. They deliver seamless personalised shopping experiences to over 29m international shoppers, who in turn generate €22.9bn revenue.


With 2,000 employees spread across 50 countries, they integrate with 300,000 point of sale systems in a number of luxury retailers and brands including Harrods, Selfridges, John Lewis, Liberty’s, Apple, Cartier, De Beers, Hermès, Rolex, Dior and Jimmy Choo.


Their products include tax-free shopping, smart data and intelligence, marketing and sales, POS technology and payment solutions.


The role


The Data Analyst will help the business to understand customer needs and behaviours through the analysis of complex data sets and subsequent translation into meaningful, shareable insights and stories.


This role will be hands on with building visualisations in Tableau and ad hoc analysis (using SQL) to inform the optimisation of products and features.


As a Data Analyst, you will:


  • Monitor and report on key KPIs.
  • Deep-dive (using SQL) into customer data to provide product teams with detailed customer analysis.
  • Be hands-on with building visualisations in Tableau.
  • Identify data requirements, working with international data teams to ensure data is cleansed and prepared.
  • Support with the identification of new and innovative ways to leverage data to deliver new customer engagement opportunities, optimise channels and deliver economic efficiency.
  • Deliver enhanced customer profiling and segmentation.
  • Measure the effectiveness of customer-facing products, features and campaigns.
  • Provide ad-hoc analysis to inform the optimisation of customer-facing products, features and campaigns.
  • Advise on how advanced statistical and analytical techniques can further improve the understanding of customers.


To apply, you should have


  • Experience working as a Data Analyst with good exposure to statistical methodologies.
  • Intermediate SQL skills.
  • Advanced experience with Tableau (or a similar tool).
  • Advanced Excel skills.
  • Proven experience delivering impactful and actionable insights with a B2C environment.


The perks include


  • 25 days holiday + bank holidays.
  • An extra day off for moving to a new house; 2 days off for your wedding; 3 days off for charity / community days.
  • Private healthcare and medical cashback plan.
  • Perkbox.
  • Competitive pension plan.
  • Virgin gym membership.


Match Digitalspecialises in connecting talented individuals with businesses in the digital, tech, media and marcomms industries.

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data analyst

Data Analyst

Data Analyst

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Machine Learning Leadership for Managers: Strategies to Motivate, Mentor, and Set Realistic Goals in Data-Driven Teams

Machine learning (ML) has become an indispensable force in the modern business world, influencing everything from targeted marketing campaigns to advanced medical diagnostics. As industries integrate predictive algorithms and data-driven decision-making into their core operations, the need for effective leadership in machine learning environments has never been greater. Whether you’re overseeing a small team of data scientists or spearheading an enterprise-scale ML project, your leadership style must accommodate rapid innovation, complex problem-solving, and diverse stakeholder expectations. This guide provides actionable insights into how you can motivate, mentor, and establish achievable goals for your machine learning teams—ensuring they thrive in data-driven environments.

Top 10 Books to Advance Your Machine Learning Career in the UK

Machine learning (ML) remains one of the fastest-growing fields within technology, reshaping industries across the UK from finance and healthcare to e-commerce, telecommunications, and beyond. With increasing demand for ML specialists, job seekers who continually update their knowledge and skills hold a significant advantage. In this article, we've curated ten essential books every machine learning professional or aspiring ML engineer in the UK should read. Covering foundational theory, practical implementations, advanced techniques, and industry trends, these resources will equip you to excel in your machine learning career.

Navigating Machine Learning Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Machine learning (ML) has swiftly become one of the most in-demand skill areas across industries, with companies leveraging predictive models and data-driven insights to solve challenges in healthcare, finance, retail, manufacturing, and beyond. Whether you’re an early-career data scientist aiming to break into ML, a seasoned engineer branching into deep learning, or a product manager exploring AI-driven solutions, machine learning career fairs offer a powerful route to connect with prospective employers face-to-face. Attending these events can help you: Network with hiring managers and technical leads who make direct recruitment decisions. Gain insider insights on the latest ML trends and tools. Learn about emerging job roles and new industry verticals adopting machine learning. Showcase your interpersonal and communication skills, both of which are increasingly important in collaborative AI/ML environments. However, with many applicants vying for attention in a bustling hall, standing out isn’t always easy. In this detailed guide, we’ll walk you through how to prepare meticulously, pitch yourself confidently, ask relevant questions, and follow up effectively to land the machine learning opportunity that aligns with your ambitions.