Document Controller

East Didsbury
10 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Warehouse Developer

Data Analyst

Data Engineer | Outside IR35 | £400 - £500 | 6 months | Hybrid Nottingham

Environmental Data Analyst Level 4 Apprentice

Kenna Recruitment has a new exciting opportunity for an experienced Document Controller to join one of the UK's Leading Residential Developers in their new region in Manchester.

Overview:

A key member of the project team, you will be responsible for the management and maintenance of all project related information and documentation, the structure and maintenance of the project common data environment (CDE), the project WIP environment and other Information Management systems where applicable.

As the sole Document Controller for this region,you will act as the central point of contact in all matters in relation to the project information management process including providing training, support, and ongoing communication with all members of the project team.

Key Respsonsbilities:

  • Admin and Office management duties

  • Dealing with contractors and suppliers

  • Data entry, research and analysis, reporting

  • Review and maintain the accuracy of the records, editing where necessary to ensure they are up to date

  • Liaising with and distributing project-related information with all levels of the project team and potentially external parties

  • Manage the processes around documentation within the organisation

  • Technically minded about naming conventions

  • Managing version control

  • Managing Asite and DocHosting systems

  • Managing LABC, Premier Guarantee and Linesearch portals

  • Creating document templates

  • Converting information from project teams into user-friendly documents

  • Numbering and labelling documents for identification and reference

  • Scanning, copying, and distributing documents to project team members and stakeholders

  • Tracking documents to maintain confidentiality

  • Reviewing documents and making revisions for accuracy

  • Liaising with project team members to ensure documents meet requirements

  • Training / Mentoring employees on document systems used internally (Asite, DocHosting)

  • Generate reports and monitor data analytics to provide feedback to the project team on project performance throughout the project lifecycle

  • Ongoing monitoring and governance of the file structure of the CDE and the project shared drive, monitoring document submission performance progress, coordinating the review process and ensuring deadlines are met

    Experience/Knowledge:

  • Ideally residential background with an understanding of the industry

  • Construction site/office based experience

  • Good practical experience and application of ISO19650 and PAS 8672

  • Good level of ASITE admin/user experience

  • Experience in implementing at either project, region or group-level Information Management Strategies

    What we can offer you:

  • Salary between £35,000 - £40,000

  • Private healthcare

  • Pension scheme (5%)

  • Life assurance

  • 25 days annual leave plus bank holidays

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.