Maths Teacher

Nant-y-bwch
9 months ago
Applications closed

Related Jobs

View all jobs

Level 5 Data Engineer Apprentice

Unlock the Equation to Success!

Are you ready to add up the excitement and multiply your impact on young minds? We're on the lookout for passionate and dynamic Math whizzes to join our stellar team of educators at Supply Desk, the education recruitment specialists!

The Sum of the Role:

Position: Math Teacher Extraordinaire
Location: Various client schools - your future classroom awaits!
Requirements: QTS (Qualified Teacher Status), an enhanced DBS, and EWC registration – the ABCs of our dream team!
Your Mission, Should You Choose to Accept:

Inspire Minds: Turn numbers into a language of wonder and excitement. Show students that math is not just a subject; it's a thrilling adventure waiting to be explored!
Create a Math Haven: Make your classroom the go-to place for all things math-tastic. Transform theories into practical, engaging lessons that spark curiosity and ignite a passion for numbers.
Be a Mathlete Mentor: Guide and support students through their mathematical journey. Foster an environment where questions are welcomed, mistakes are celebrated, and learning is a lifelong adventure.
Collaborate and Conquer: Work closely with colleagues and client schools to create a synergy that adds up to success. Share your math magic and contribute to the collaborative spirit that defines Supply Desk.
Why Join Us?

Math-Mazing Opportunities: Your expertise will shape the future mathematicians of tomorrow. From algebra antics to geometry giggles, you'll make learning an epic adventure!
Countless Benefits: We've got the perks that will make your heart skip a beat! Competitive salaries, ongoing professional development, and a chance to be the algebraic superhero you were born to be.
Team Spirit: Join a community of educators who value collaboration and support. We believe in the power of teamwork – it's the sum of all our parts that makes us extraordinary!
Fun, Fun, Fun: At Supply Desk, we believe learning should be fun, and we apply that principle to our work culture too! Expect a vibrant, lively atmosphere that celebrates success and values each member.
Apply Now and Let the Numbers Align!

Ready to join the squad? Send your CV, along with a math-inspired cover letter, to , or call our dedicated team on . Don't miss out on the chance to be part of an educational revolution where learning is a joy, and math is the coolest subject in the book!

Supply Desk is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all teachers and students. Apply today and help us shape the future, one equation at a time

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 Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

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

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

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

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.