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For Companies At a Glance
- Tasks: Analyse mobile network data and build automated production pipelines.
- Company: Leading global professional services firm in Greater London.
- Benefits: Dynamic work environment with innovation and collaboration opportunities.
- Why this job: Drive business improvements while working on exciting data projects.
- Qualifications: 4+ years of SQL, 2+ years in Python, and Agile understanding.
- Other info: Great professional development opportunities await you!
The predicted salary is between 43200 - 72000 £ per year.
A leading global professional services firm in Greater London is seeking a Data Engineer for their Movement Analytics team. The successful candidate will analyse mobile network data and build automated production pipelines, focusing on driving business improvements.
Ideal applicants should have:
- 4+ years of SQL experience
- 2+ years in Python
- A solid understanding of Agile methodologies
The role offers a dynamic work environment emphasizing innovation, collaboration, and professional development opportunities.
Movement Analytics Data Engineer: Build Scalable Pipelines in London employer: GHD
Contact Detail:
GHD Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Movement Analytics Data Engineer: Build Scalable Pipelines in London
✨Tip Number 1
Network like a pro! Reach out to folks in the Movement Analytics field on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your SQL and Python projects. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on Agile methodologies and be ready to discuss how you've used them in past projects. We want to see your problem-solving skills in action!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Movement Analytics Data Engineer: Build Scalable Pipelines in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your SQL and Python experience, as well as your understanding of Agile methodologies. We want to see how your skills align with the Movement Analytics team’s needs!
Craft a Compelling Cover Letter: Use your cover letter to tell us why you’re passionate about data engineering and how you can contribute to driving business improvements. Be genuine and let your personality shine through!
Showcase Relevant Projects: If you've worked on any projects that involved building automated production pipelines or analysing mobile network data, make sure to mention them. We love seeing real-world applications of your skills!
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and get back to you quickly!
How to prepare for a job interview at GHD
✨Know Your SQL Inside Out
Since the role requires 4+ years of SQL experience, make sure you brush up on your SQL skills. Be prepared to discuss complex queries you've written and how they've impacted previous projects. Practising common SQL interview questions can really help you stand out.
✨Show Off Your Python Skills
With 2+ years in Python being a must, think about specific projects where you've used Python to build automated pipelines. Be ready to explain your coding process and any challenges you faced. A little live coding practice could go a long way!
✨Embrace Agile Methodologies
Understanding Agile is key for this position. Familiarise yourself with Agile principles and be prepared to discuss how you've applied them in past roles. Sharing examples of how Agile has improved team collaboration or project outcomes will show you're a great fit.
✨Highlight Your Problem-Solving Skills
This role focuses on driving business improvements, so be ready to talk about how you've tackled data-related challenges in the past. Think of specific instances where your analysis led to actionable insights or significant changes. This will demonstrate your value to the team.
