E-Commerce Data Analyst Hybrid

Southwell

3 months ago

Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Data Analyst (Salesforce & Excel)

Databricks SME and AWS Data Engineer

Senior Data Engineer (Microsoft Fabric)

ERP Data Analyst

Junior Data Analyst

MinsterFB is seeking a highly numerate, digitally savvy E-Commerce Data Analyst to support our continued growth. You’ll play a key role in delivering insights that drive performance for some of the UK’s most beloved brands on Amazon—including Grenade, Bisto, Yorkshire Tea, McVitie’s, and Cadbury.

What You’ll Do

Collaborate with cross-functional teams to analyse data, generate reports, and deliver actionable insights that support profitable growth for our clients
Work with a tech stack that includes AWS, ZOHO, and other industry-leading tools
Combine data from multiple sources to support strategic decision-making, specialising in either commercial or operational areas

What You’ll Bring

Strong analytical and numeracy skills
Proficiency in Excel; working knowledge of SQL and/or Python is a plus
Ability to work independently and prioritise workload
Clear and confident communication skills

Qualifications

Degree-level education, ideally with strong mathematical ability
Affinity for digital technologies and online platforms
---

Personal Qualities

We’re looking for someone who is:

Enthusiastic about data and digital innovation
Detail-oriented and methodical
Curious, adaptable, and eager to learn
A team player with a proactive mindset
Analytical, with a knack for identifying key metrics
---
Location & Flexibility

This role is based in our Southwell office at least two days per week
Remote work is supported, including up to 4 consecutive weeks per year from anywhere in the world
A 3-month unpaid sabbatical is available after 4 years of continuous employment
For the first 4 weeks, you’ll be in the office daily to get to know the team and our ways of working
Please apply only if you can commit to the in-office requirement
Benefits

33 days annual leave (including public holidays)
3pm Friday finish
Access to a 24/7 employee assistance programme (GP consultations, counselling, legal and financial advice)
Quarterly team and charity days
A range of additional employee perks
About MinsterFB

MinsterFB is a Certified B Corporation, part of a global community of businesses that meet high standards of social and environmental impact. We provide full Amazon account management, sales strategy, catalogue optimisation, issue resolution, and training. Our success is rooted in deploying every growth tool available to Amazon Sellers and Vendors.

Hours: Monday–Thursday 9am–5:30pm, Friday 9am–3pm

How to Apply

Please attach your CV via the link provided. To ensure your application is reviewed, include the phrase:

“I am able to work 2 days a week in Southwell” in the subject line of your application.

Diversity & Inclusion

MinsterFB values a diverse workforce. We encourage applications from women, people of colour, individuals with disabilities, and members of the LGBTQ+ community. We believe that an inclusive and empowered team is key to achieving our mission. If you need accommodations during the recruitment process or have feedback on how we can make it more accessible, please let us know

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

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

Feb 10, 2026

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.

Feb 3, 2026

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.

Jan 23, 2026

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.