Senior Data Analyst - Saint Eval

Rick Stein
Wadebridge
3 months 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 - Saint Eval

Senior Data Analyst

  • 40,000- 45,000 per annum.
  • Plus, up to 2,400 per year in tips (paid weekly, based on last year's earnings), giving total potential earnings of 47,400 per year.
  • Permanent Contract 40hr week.

We're looking for a talented and driven Senior Data Analyst to play a key role in transforming how we use data to make smarter business decisions. You'll be responsible for ensuring the consistency, accuracy, and integrity of our data across multiple operational systems, developing insightful analysis, and building robust forecasting models that directly support commercial and operational performance.

This is a fantastic opportunity for someone who thrives on problem-solving, enjoys working collaboratively, and wants to make an impact by improving the way data informs strategy and drives performance.

Duties and Responsibilities

  • Create and maintain a consistent data structure across all operational systems (Access, Fourth, Mapal, Alert65), establishing rules and audit checks to ensure ongoing compliance and data integrity.
  • Oversee performance reporting and analysis, translating operational and business data into actionable insights.
  • Build reliable analytical models to improve forecasting and demand planning for centralised stock, providing recommendations to the commercial team.
  • Develop performance forecasting templates to enhance both financial and non-financial decision-making.
  • Produce clear training materials and "how-to" guides to maintain high data quality across teams and systems.
  • Support supplier onboarding by ensuring catalogue data is accurate and consistent.
  • Maintain and manage data across core systems, including: Products & promotions in EPOS, Recipes and product catalogues in PW
  • Introduce and integrate third-party data sources to enhance business insight and decision-making.
  • Identify opportunities for efficiency through automation, AI, improved structure, and governance.
  • Monitor market trends and performance, providing comparative analysis and actionable recommendations to enhance competitiveness.

Experience:

  • Proven experience in a data analysis or business intelligence role, ideally within a commercial or operational environment.
  • Strong skills in data modelling, reporting, and visualisation (e.g. Power BI, Excel, SQL).
  • Excellent attention to detail and commitment to maintaining high data quality.
  • Experience managing data across multiple systems or platforms.
  • Strong communication skills, with the ability to explain insights to non-technical stakeholders.
  • Proactive, collaborative, and adaptable approach to problem-solving.

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.