Finance Data Analyst

Fareham
6 months ago
Applications closed

Related Jobs

View all jobs

Finance Data Analyst

Finance Data Analyst

Finance Data Analyst

CAPEX Reporting Data Analyst - Reading, Berkshire

Senior Data Engineer

2 x Data Analyst - Local Authority

A dynamic tech company in Fareham are looking for a Finance Analyst with skills in SQL to join their FP&A team, and support decision-making through financial analysis and reporting.

You'll work with large, complex data sets to uncover key trends and inform critical business strategies, with the opportunity to get involved in high-level financial planning.

This will include developing financial models and forecasting tools to predict the impact of business changes, new product launches, pricing strategies and market shifts, and creating comprehensive financial reports and visualisations using Looker.

You'll be working with data from Google BigQuery, though prior experience with this is not required, as long as you bring SQL skills for data extraction and manipulation, and a desire to learn how to work with new technologies.

It's an exciting time to join this company as they continue to experience incredible growth and success, with excellent opportunities for internal progression!

This is a fully office-based role in the Fareham area - they have a highly collaborative and close-knit culture, are very social, and there's a great team spirit running throughout the organisation. Their office is incredibly modern, with perks including an on-site gym, free meals and more.

Requirements

Experience in an Analytical role working with financial data
Experience with forecasting and building financial models would be beneficial but not essential
Experience with SQL for data extraction and manipulation
Experience with data visualisation tools such as Google Looker, Tableau, Power BI etc.
Experience working with Google Big Query would be advantageous but not essential
Excellent communication skills / ability to liaise with different teamsBenefits

Salary up to £45,000 depending on experience
Pension scheme with matched contributions up to 5%
20 days annual leave + plus bank holidays + plus birthday off (increasing with service, and option to purchase an additional 5 days per year)
Health and dental after one year of service
A huge range of office perks including free gym, free breakfast & lunch, coffee etc

Please Note: This is a permanent role for UK residents only. This role does not offer Sponsorship. You must have the right to work in the UK with no restrictions. Some of our roles may be subject to successful background checks including a DBS and Credit Check.

Tenth Revolution Group / Nigel Frank are the go-to recruiter for Power BI and Azure Data Platform roles in the UK, offering more opportunities across the country than any other. We're the proud sponsor and supporter of SQLBits, and the London Power BI User Group. To find out more and speak confidentially about your job search or hiring needs, please contact me directly at

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.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.