Build Preparation Coordinator

Gaydon
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Data analyst

ERP Data Analyst

Data Analyst

Data Engineer

Data Analyst

Data Scientist

Our OEM Client based in Gaydon, is searching for a Build Preparation Coordinator to join their team, Inside IR35. This is a 12-month contract position initially until 31st March 2026, with the potential for further extensions.

Umbrella Pay Rate: £27.03 per hour.

Duties:

The purpose of this role is to contribute to the development and delivery of business processes for the build operations, resource and capacity planning for all programmes, build site and unit moves logistics.
You’ll also help in identifying and delivering continuous improvement projects for our build facility, whilst managing day to day responsibilities of direct reports
The Build Preparation Coordinator role is varied on a day-to-day basis and best suits an individual capable of managing several tasks simultaneously.
In this role, you’ll establish and maintain key cross-functional relationships within the business due to the varied functions in which we collaborate.
Job Description:

This position requires work onsite.
Responsible for managing the shortage parts post allocation and managing the movement of those parts between stores and build zones.
Responsible for the management of the missing damaged wrong process within the build operations.
Coordinating rework activities through material withdrawal process and managing the correct process of parts are returned to support the build.
Coordinate various stakeholders such as Stores, NRP, Supply Chain, Build Planning and Build Operation, to ensure part kits are complete and support resolution of issues.
Investigating root cause of any part pick issues with problem solving methodology.
Coordinate, deliver and maintain operational processes ensuring deliverables meet strategic objectives of the area.
Coordination of parts returned from build and investigation into the reason parts aren’t required and utilising those parts back to build.
Creating and managing pick kit Matrix for each build and updating on Wrike
Interface with key stakeholders ensuring that service and process objectives are delivered efficiently.
Systems investigations into overall parts queries and shortages (GPIRS)
Shortage parts Escalation points for any risks to build operations.
Attend reasonable requests from the area Management or Lead levels.
Essential Skills:

Competent IT skills to support data analysis and produce reports, intermediate/advanced skills in Microsoft Excel (VBA and Power Query if possible), Tableau or Power BI, Wrike or Microsoft Project.
Understanding of parts tracking via GPIRS / SAP (awareness) or similar system to control Bill of Materials.
Proficient communication and organisation skills to work with multiple simultaneous tasks and driving license for occasional travel between Whitley & Lyons Park.
Desirable Skills:

Experience in a logistics and parts environment.
Experience in stakeholder management.
Proficient in problem solving.
Data driven profile, with experience with excel reports, crossing data from multiple sources using formulas, pivot tables and charts.
Education:

Educational Background on Data Analytics, Production/Manufacturing Engineering, Logistics or Administration.
Desired IT Background:

Intermediate to Advanced Skills on excel (VBA, Power Query, Custom Functions and other)
Business Intelligence Knowledge, capable to produce dashboards from scratch and work in the maintenance (Power BI, Tableau or any other similar tool)
Project Planning software skills, like Microsoft Project, Oracle P6, Wrike or similar tool

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.

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.