Geoenvironmental Engineer

Winchester
10 months ago
Applications closed

My client is a multi discipline Property and Construction Consultancy, who help their clients create better, more sustainable place for people to live, learn and work.
 
The team in Winchester are seeking a Geo-Environmental Engineer to work on new and existing projects in southern England, mainly comprising Phase 1 and Phase 2 Site Investigations. The position is based within the GeoEnvironmental Consultancy Department.
 
The Role
You will have the opportunity to work on a range of different projects mostly comprising redevelopment of brownfield sites. You will have the opportunity to become a key member of the team, working with the senior staff on some exciting projects.
Role and Responsibilities
·Planning, execution and reporting of Phase 1 and Phase 2 Geo-Environmental and Geotechnical site investigations
·Logging soil and rock samples
·Scheduling laboratory testing
·Site monitoring - groundwater, surface water and ground-gas
·Conducting soil and groundwater risk assessments Liaising with Clients and Regulators
·Production of Remediation Method Statements, supervision of remediation works and production of Validation Reports
Experience and Skills Required
·Relevant degree in Geology, Environmental Science - postgraduate degree desirable
·Experience working for a UK multi-discipline consultancy
·Excellent communication and report writing skills
·Membership of a professional organisation such as the Geological Society or CIWEM, ideally working towards Chartered Status
·Experience of quantitative human health, groundwater and ground-gas risk assessments
·Geotechnical knowledge, logging soils and rocks etc.
·Full driving licence
 
The Geo-Environmental Department provides a diligent, friendly working environment where staff are encouraged to research and be innovative in their work.
 
What We Can Offer
 
This role offers you the chance to progress your career at a forward thinking, friendly company. Along with providing you with an opportunity to learn and develop skills and qualifications as part of a dynamic team, we also offer;
·Ability to contribute to the growth and development of the team, adding value to our growing business
·25 days holiday - entitlements increase by 1 day per annum after 3, 5 and 8 years
·Private Healthcare
·Life Assurance
·Standard 6% Ridge Contribution - rising to 7% after 5 years, and 8% after 10 years
·Highly competitive salary
·Salary Sacrifice - Cycle to Work Scheme
·Buying and Selling Annual Leave
·Gym Membership Scheme
·Company car leasing scheme
 
When you join the company, you will be surrounded by professional people who are experts in their field. You will work with and be surrounded by mentors and a support network to help you to embrace new challenges, grow, develop your skills and experience to progress with your career.
 
This company has climbed to 14th position in the Building Magazine Top 150 Consultants, whilst continuing to rise within the Top 50 Architects, Engineers, Project Managers, and Surveyors lists - with us in the top 10 surveyors in the country. Showcasing our continued success

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

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.