Data Analyst

WEX Europe Services Limited
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

DATA ANALYST / MANCHESTER / HYBRID/ PERMANENT/ £40,000-£45,000 PLUS BENEFITS

About the Team / Role

WEX Europe Services Ltd are the owner of the Esso Card Fuel Card Portfolio, and with offices across Europe and the US are one of the Europe’s largest providers of fuel cards.

The data Analyst will be responsible for analyzing financial data, forecasting future financial trends, and providing recommendations to improve financial performance. The role involves creating financial models, conducting variance analyses, and preparing reports to assist in decision-making processes across the organization.

This is an exciting opportunity for the successful candidate to make lasting change within the business and be part of the growing the business.

What’s on Offer?

Highly Competitive salary of £40,000-£45,000 (Dependant on experience) Annual company bonus 37.5 hour week- Monday to Friday, no evenings or weekends Hybrid working from our Manchester City Centre office (1-2 days per week) Industry leading pension scheme 25 days holiday plus bank holidays- with the opportunity to purchase additional holidays Life assurance Income protection Discount & Perks platform Employee wellbeing

How you’ll make an impact

Financial Analysis: Analyze financial data to identify trends, opportunities, and risks, ensuring insightful reporting. Budgeting & Forecasting: Assist with the development of annual budgets and quarterly financial forecasts. Variance Analysis: Conduct monthly and quarterly variance analysis between actuals and forecasts/budgets. Financial Modeling: Create detailed financial models to support strategic initiatives, capital investments, and business decisions. Reporting: Prepare Retention and Sales reports, including profit and loss statements, balance sheets, and other performance metrics for management. Business Insights: Provide actionable insights to optimize costs, revenue, and overall financial performance. Data Analysis: Gather, analyze, and interpret financial data to support decision-making. Cross-Functional Collaboration: Partner with departments such as Sales, Operations, and Marketing to monitor budgets and performance. Ad-Hoc Analysis: Support leadership with special projects, financial studies, and operational analyses. Program Management: Mange new workstreams of revenue.

Experience you’ll bring

Ideally educated to degree level or qualified by experience Previous experience and knowledge of working with SQL (essential), Power BI, Informatica and Python Ability to work cross functionally across the business including with key stakeholders and Senior Leadership Team Ability to undertake project work and understand Lean 6 Sigma or Agile. Would be of a strong advantage if applicants have previous experience of working with finance systems such as Card 1, ICFS, AR or payment systems

What’s Next?
If you have the skills and passion to take on this position of DATA ANALYST, then we would love to hear from you. APPLY NOW for immediate consideration.

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.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.