CFSI & Quality Lead for Nuclear Projects in Bristol

CFSI & Quality Lead for Nuclear Projects in Bristol

Bristol Full-Time 50000 - 65000 Β£ / year (est.) No working from home possible
CWA

At a Glance

  • Tasks: Lead a team ensuring supplier quality for the Sizewell C project.
  • Company: CWA, a leader in nuclear project management.
  • Benefits: Competitive salary, bonus scheme, and robust pension plan.
  • Other info: Enjoy a hybrid working model with travel opportunities across major UK cities.
  • Why this job: Make a significant impact on nuclear projects while leading a dedicated team.
  • Qualifications: Experience in quality management and team leadership required.

The predicted salary is between 50000 - 65000 Β£ per year.

CWA is recruiting a CFSI Manager to oversee supplier quality within the Sizewell C project. This permanent, full-time role offers a hybrid working model with travel required between London, Manchester, and Bristol.

The successful candidate will lead a team of Quality Engineers, ensuring compliance with quality systems and effectively managing risks.

A competitive salary and bonus scheme are included, along with a robust pension plan.

CFSI & Quality Lead for Nuclear Projects in Bristol employer: CWA

CWA is an exceptional employer, offering a dynamic work environment that fosters collaboration and innovation within the nuclear sector. With a strong commitment to employee development, we provide ample opportunities for growth and advancement, alongside a competitive salary, bonus scheme, and a comprehensive pension plan. Our hybrid working model allows for flexibility, making it easier for our team members to balance their professional and personal lives while contributing to impactful projects like Sizewell C.

CWA

Contact Details:

CWA Recruitment Team

We think you need these skills to ace CFSI & Quality Lead for Nuclear Projects in Bristol

Supplier Quality Management
Quality Systems Compliance
Risk Management
Team Leadership
Project Oversight
Communication Skills
Hybrid Working Model Adaptability