NDT Inspector

South Wigston

9 months ago

Applications closed

Related Jobs

View all jobs

Data Engineer

NDT Inspector required in South Leicester. 3 x shifts (no weekends), days whilst training. Permanent, Immediate start available. 37 hours per week £18.04 per hour inc shift allowance, + Bonus, Overtime available.

We have recruited for this growing Manufacturer of components for the aerospace and power generation industries for over 20 years. They have a very professional reputation offering great working conditions and future prospects.

NDT Inspector role:

To complete fluorescent penetrant inspection, visual inspection, and dimensional inspection on Aerospace and industrial components in line with strict procedures and specifications.

Requirements for NDT Inspector:

Hold a level 1 / 2 FPI qualification
Proven NDT experience ideally gained on Aerospace products
A solid understanding of engineering drawings and method specifications
Ability to use conventional inspection equipment
Ability to use CMM machines
A sound understanding of inspection techniques (on a variety of products) and proven engineering experience
Experience of inspecting to tight tolerances in a precision engineering environment
Experience of working in a fast-paced manufacturing Company, with demanding targets
Ability to produce quality work whilst working under pressure
Able to work with little or no supervision

NDT Inspector Responsibilities:
Process work in line with requirements
Inspect work, ensuring that strict conformance is met
Sign off batch cards detailing components are in line with specification and meet customer requirements
Raise any conformance issues and any relevant paperwork in line with Company procedure
To maintain FPI level 1 / 2 accreditation as required.
First off inspections
Receipt inspection (validation of parts returning from subcontract locations)
Complete conventional and CMM inspection work as an independent over check for parts
Understand engineering drawings for the purpose of inspection
Complete visual inspection of parts during the inspection process
Undertake airflow Inspection tasks as required
Consistently achieving all area targets as determined by the Cell Manager
Adherence to all company policies and procedures, including SOX, Code of Conduct and Health and Safety.

Hours

37-hours per week – 3 shift pattern (morning, afternoon and night rotation)

Morning Hours
05:45 – 13:45 Monday – Thursday
05:45 – 10:45 Friday

Afternoon Hours
13:45 – 21:45 Monday – Thursday
10:45 – 15:45 Friday

Night Hours
21:45 – 05:45 Monday – Thursday
15:45 – 20:45 Friday

Holidays

26 days floating + 7 statutory days

Overtime

Overtime available and paid in line with agreed rates

Please note: This company cannot support sponsorship at this time. Applicants must have proof of Right to Work in the UK

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 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.

Feb 3, 2026

Jobs

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.

Jan 23, 2026

Education

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.