Civils Estimator – Groundworks & Piling (Hybrid)

Energy Jobline CVL; Manchester; Híbrido; GBP 45.000 - 65.000; Jornada completa

Descripción de la vacante

Energy Jobline CVL is seeking an experienced Estimator focused on civils, groundworks and piling, to price projects and prepare tenders. The role supports a hybrid working pattern with travel to client meetings, sites and the office as required.

The ideal candidate will have a technical background, UK work experience in civils, and the ability to develop tender strategies while maintaining strong client relationships and data-driven estimates.

Formación

• Degree or HNC in a related field with UK work experience
• Sponsorship not available; eligible to work in the UK
• Commercially astute with excellent written and verbal communication skills
• Able to work under pressure and meet tight deadlines
• Develop tender strategy considering client, design, and project constraints
• Develop strong client relationships across all levels
• Attention to detail from multiple data sources

Responsabilidades

• Secure work in line with strategy and budgeted margins
• Ensure tenders and bids are reviewed and approved within governance
• Develop relationships with clients for tenders in hand and future opportunities
• Seek continuous improvement of tender and bid success rates
• Ensure tender submissions are of the highest standard and submitted on time

Conocimientos

Estimating software

Tendering experience

Microsoft Excel

Client relationships

Civil engineering knowledge

Educación

Degree in related field

Herramientas

Estimating systems

Descripción del empleo

Energy Jobline CVL is seeking an experienced Estimator focused on civils, groundworks and piling, to price projects and prepare tenders. The role supports a hybrid working pattern with travel to client meetings, sites and the office as required.

The ideal candidate will have a technical background, UK work experience in civils, and the ability to develop tender strategies while maintaining strong client relationships and data-driven estimates.