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

HR Senior Data Analyst

Senior Power BI Data Analyst

Senior Data Engineer

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.

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.