Maintenance Planner

West Thurrock
10 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst & Property Records Officer

Data Analyst & Property Records Officer

Data Analyst

Reporting & Data Analyst

Data Analyst

Junior Data Engineer

Maintenance Planner & Scheduler

📍West Thurrock, RM19
đź’°ÂŁ45,000 per annum (DOE)
🕒Monday to Friday, 8am–4pm (35 hours/week)

Overview:
We’re working with a leading manufacturing business in West Thurrock to recruit a Maintenance Planner and Scheduler. This pivotal role within the engineering department acts as the central point of contact between teams, driving planning and performance across a well-established and well-equipped site.

This position would suit someone with a strong background in maintenance planning within a manufacturing or industrial environment.

Salary & Benefits:

Salary: ÂŁ45,000 (DOE)

Bonus: Up to 12%, based on personal and company performance

Private healthcare

28 days annual leave plus bank holidays

Company sick pay (after 6 months' service)

Life Assurance

Defined Contribution Pension Plan

Income Protection (for DC members)

Long Service Awards

Health & Wellbeing Programme

Flu and Eye Care Vouchers

Reward Hub access

Cycle to Work Scheme

Working Hours:

Monday to Friday, 8am–4pm (1-hour break)

On-call rota: 1 in 5 rotation (tech support, remote or call only – very rarely site attendance). This would only be required once fully up to speed.

Key Responsibilities:

Convert maintenance requests into work orders and build detailed planning schedules

Daily engagement with the plant to assess maintenance needs and develop plans accordingly

Coordinate and plan routine and reactive maintenance activities

Lead weekly and monthly planning meetings and produce completion reports

Collaborate with the stock and parts controller to order and identify required items

Support contractor management – onboarding, inductions, documentation, and monitoring their work

Act as the CMMS (SAP Hana) subject matter expert, maintaining data integrity and optimising maintenance plans

Assist with office and facility-related maintenance requirements as needed

No direct reports but significant cross-departmental interaction

The Ideal Candidate:
âś… Minimum 5 years' experience in a similar planning/scheduling role within a manufacturing or industrial setting
âś… Engineering qualification or completed an apprenticeship in a relevant discipline
âś… Strong experience using CMMS systems (SAP Hana ideally)
âś… Familiarity with maintenance methodologies such as preventive/proactive/RCM
âś… Competent in project planning tools (e.g., MS Project) and analysing data using spreadsheets and databases
âś… Understanding of mechanical, electrical, pneumatic, and hydraulic systems
✅ Knowledge of health and safety legislation – LOLER, PUWER, COSHH, etc.
âś… Experience managing contractors, spare parts, and budgets
âś… Strong communication skills with the ability to lead meetings and act as a central coordinator

Desirable Extras:

Forklift or Cherry Picker licence

Lean Manufacturing training/qualification

AutoCAD (2D drawing/schematics)

NEBOSH or IOSH certification

Experience using GANTT charts

Team & Culture:
You’ll be part of a 10-person engineering team, on a site employing around 45 people. This role is truly the bridge between departments, supporting the Maintenance Manager and working closely with colleagues across the plant.

You’ll share an office with one other person and be based on site five days a week, with potential flexibility for occasional remote work once settled.

Ready to take the lead in maintenance planning for a site that values structure, efficiency, and collaboration? Apply today to find out more

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.