At a Glance
- Tasks: Analyse aircraft data to enhance maintenance strategies and improve system performance.
- Company: Leading aerospace organisation focused on innovation and reliability.
- Benefits: Competitive pay, contract role, and the chance to work with advanced technologies.
- Other info: Opportunity for growth in a dynamic aerospace environment.
- Why this job: Make a real impact in aviation by improving fleet diagnostics and predictive maintenance.
- Qualifications: Experience with Palantir Foundry, Jupyter Notebook, and strong data analytics skills.
The predicted salary is between 3000 - 4000 € per month.
A leading aerospace organisation is seeking a Data Analyst / Health Engineer to support the development of fleet diagnostic and predictive maintenance capability across a global aircraft in service environment. This role sits within an advanced engineering and data function, working directly with aircraft operational data to improve reliability, system performance and maintenance decision making.
The successful candidate will work across engineering, design and fleet support teams to deliver high impact analytical outputs that directly influence aircraft health monitoring and predictive maintenance strategy.
Key Responsibilities:- Contribute to development of fleet diagnostic and predictive maintenance models
- Support technical investigations using in service aircraft data
- Deliver structured data studies for engineering and design office stakeholders
- Translate complex operational datasets into actionable engineering insight
- Support wider data management and analytics transformation initiatives
- Improve understanding of in service fleet behaviour and system performance
- Strong experience with Palantir Foundry / Skywise
- Advanced Jupyter Notebook capability
- Jira and Github in engineering or data driven environments
- Strong data analytics and visualisation capability
- Confident stakeholder management across technical teams
- Delivery focused, independent working style
- Skywise Predictive Maintenance (SPM)
- Aircraft systems or maintenance engineering understanding
- Experience in fleet diagnostics or predictive maintenance environments
- Exposure to AOG / operational disruption contexts
- Familiarity with structured maintenance processes and reliability frameworks
- Machine learning or AI applied to engineering datasets
This is a high impact opportunity within a leading aerospace environment, contributing directly to the evolution of predictive maintenance capability on in service aircraft. Confidential discussions available for suitably experienced candidates.
Data Analyst in Gloucester employer: Meritus
Join a leading aerospace organisation that prioritises innovation and employee development in the heart of Filton. With a collaborative work culture, you will have the opportunity to engage with cutting-edge technology while contributing to impactful projects that enhance aircraft reliability and performance. The company offers competitive pay, a supportive environment for professional growth, and the chance to work alongside industry experts in a dynamic setting.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst in Gloucester
✨Tip Number 1
Network like a pro! Reach out to people in the aerospace industry, especially those working with data analytics. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data analysis projects, especially those using Palantir Foundry or Jupyter Notebook. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and stakeholder management skills. Be ready to discuss how you've used data to drive decisions in past roles, as this is key for the Data Analyst position.
✨Tip Number 4
Don't forget to apply through our website! We have loads of opportunities that might be perfect for you. Plus, it shows you're serious about joining our team and makes it easier for us to find your application.
We think you need these skills to ace Data Analyst in Gloucester
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Analyst role. Highlight your experience with Palantir Foundry, Jupyter Notebook, and any relevant projects that showcase your data analytics skills. We want to see how your background aligns with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about the aerospace industry and how your skills can contribute to our predictive maintenance initiatives. Keep it concise but impactful – we love a good story!
Showcase Your Technical Skills:Don’t forget to highlight your technical skills in your application. Mention your experience with Jira, GitHub, and any machine learning or AI projects you've worked on. We’re looking for someone who can hit the ground running, so make it clear how you can add value!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy and ensures your application goes directly to us. Plus, you’ll get to see more about our company culture and values while you’re at it!
How to prepare for a job interview at Meritus
✨Know Your Data Tools
Make sure you brush up on your experience with Palantir Foundry and Jupyter Notebook. Be ready to discuss specific projects where you've used these tools, as they are crucial for the role. Highlight how you've translated complex datasets into actionable insights.
✨Understand the Aerospace Context
Familiarise yourself with the aerospace industry, especially in relation to fleet diagnostics and predictive maintenance. Being able to speak knowledgeably about aircraft systems and maintenance engineering will set you apart from other candidates.
✨Showcase Your Stakeholder Management Skills
Prepare examples of how you've effectively managed stakeholders in previous roles. This could involve discussing how you communicated technical findings to non-technical teams or collaborated with engineering and design offices to deliver impactful data studies.
✨Be Ready for Technical Questions
Expect questions that test your analytical skills and understanding of machine learning or AI in engineering contexts. Brush up on relevant concepts and be prepared to discuss how you've applied them in real-world scenarios, especially in relation to operational disruptions.
