Quality Engineer

Role: Apprentice Quality Engineer

Location: Blackpool, FY4 4NW

Hours: 37 hours per week | between 08:00 and 16:30 Monday to Thursday, and 08:00-13:00 Friday

Hourly rate:  £8.66 (£16,741.51pa)

Kick‑start your engineering and quality career with a hands‑on apprenticeship

Are you curious about how things work, enjoy solving problems, and want to build a career in engineering and quality? Our Apprentice Quality Engineer role is a great opportunity to earn while you learn, gaining real‑world experience alongside a Level 3 Data Technician apprenticeship.

You’ll work within our Supplier Quality team, supporting both internal manufacturing teams and external suppliers to improve quality, prevent defects, and drive continuous improvement across our processes. From day one, you’ll be learning on the job, supported by experienced mentors and structured training.

What you’ll be doing

As an Apprentice Quality Engineer, you’ll be involved in a wide range of quality and improvement activities, including:

• Collecting and analysing quality data to help identify trends, issues, and improvement opportunities
• Supporting investigations into non‑conforming or defective materials and helping to put corrective actions in place
• Working with suppliers to assess quality, capability, and compliance
• Supporting new product introduction and supplier verification activities
• Taking part in cross‑functional improvement projects across the business
• Learning and applying structured problem‑solving, Lean, and continuous improvement tools

You won’t be expected to know everything from day one – this role is about learning, developing, and building confidence over time.

Your apprenticeship – Level 3 Data Technician apprenticeship

Alongside your role, you’ll complete the L****evel 3 Data Technician apprenticeship, designed to help you understand how to identify, plan, and deliver meaningful improvements in a business environment.

You’ll learn how to:

• Understanding data types, sources, architecture, and how data is managed, shared, and stored
• Extracting, cleaning, validating, formatting, and combining data from multiple sources
• Data quality, governance, and compliance considerations
• Analysing data using basic statistical methods, databases (SQL), algorithms, and pattern recognition