At a Glance
- Tasks: Build high-performance computational pipelines and optimise large datasets using Python.
- Company: Join a growing team developing an award-winning SaaS analytics platform.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on innovation and collaboration.
- Why this job: Tackle technical challenges and make a real impact in the insurance industry.
- Qualifications: Strong Python skills and experience with large datasets and analytical pipelines.
The predicted salary is between 60000 - 80000 € per year.
Interested in being part of a growing, specialised team developing an award-winning SaaS analytics platform used to assess over £200bn of non-life insurance business? This is a mid to senior level software engineering role focused heavily on production-grade Python development. This is not a web-facing CRUD or API-endpoint development role. The core focus of this position is building high-performance computational pipelines, optimising memory for large datasets, and engineering mathematical/analytical engines, you will need relevant experience in this space to be considered. This is a hybrid role based in London (2 days per week in office).
The role:
- Technical Challenge: Take ownership of performance bottlenecks involving 4GB+ datasets.
- Architectural Input: Taking new features from conception to deployment.
- Production-Grade Python: Apply software engineering best practices including OOP, unit testing, and CI/CD.
- Mathematical/ Financial Logic: Translating complex analytical, mathematical, or financial logic into reusable, production-grade software tools.
What We Are Looking For:
- Production-Grade Python: Strong experience writing clean, modular, and maintainable Python code (classes/functions, separation of concerns) within a professional software engineering environment.
- Large Dataset Management: Proven track record of preventing memory overheads and optimizing execution speeds when manipulating massive (GB-scale) datasets. Focusing on performance bottlenecks via vectorisation, parallelism, chunking, or memory optimization.
- Analytical Pipelines: Experience building end-to-end analytical pipelines (data ingestion → computational transformation → modelling → output) rather than one-off analyses.
- Cloud Environments: Hands-on exposure to deploying and managing applications within a cloud environment (AWS, GCP, or Azure) and an understanding of how components fit together (API → compute → storage).
Backend Software Engineer (Python) employer: The Emerald Group Ltd, Search and Selection
Join a dynamic and innovative team in London, where your expertise as a Backend Software Engineer will contribute to the development of a cutting-edge SaaS analytics platform. We foster a collaborative work culture that values technical excellence and encourages continuous learning, offering ample opportunities for professional growth and advancement. With a hybrid working model, you can enjoy the flexibility of remote work while being part of a vibrant office environment two days a week.
Contact Detail:
The Emerald Group Ltd, Search and Selection Recruiting Team
StudySmarter Expert Advice🤫
We think this is how you could land Backend Software Engineer (Python)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best Python projects, especially those involving large datasets and analytical pipelines. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your problem-solving skills. Practice coding challenges that focus on performance optimisation and memory management. We recommend using platforms like LeetCode or HackerRank to get in the zone.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Backend Software Engineer (Python)
Some tips for your application 🫡
Show Off Your Python Skills:Make sure to highlight your experience with production-grade Python development. We want to see examples of clean, modular code you've written and how you've tackled performance bottlenecks in the past.
Talk About Big Data:Since this role involves handling large datasets, share your experiences with managing GB-scale data. Let us know how you've optimised memory usage and improved execution speeds in your previous projects.
Demonstrate Your Analytical Mindset:We’re keen on seeing how you’ve built end-to-end analytical pipelines. Describe any relevant projects where you’ve taken data from ingestion to output, showcasing your ability to translate complex logic into reusable software tools.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves.
How to prepare for a job interview at The Emerald Group Ltd, Search and Selection
✨Know Your Python Inside Out
Make sure you brush up on your Python skills, especially around production-grade practices. Be ready to discuss OOP principles, unit testing, and CI/CD processes. They’ll want to see that you can write clean, modular code, so have examples of your work handy.
✨Showcase Your Experience with Large Datasets
Prepare to talk about your experience managing large datasets. Think about specific challenges you've faced, like performance bottlenecks or memory overheads, and how you tackled them. Highlight any techniques you used, such as vectorisation or parallelism.
✨Demonstrate Your Analytical Pipeline Skills
Be ready to explain how you've built end-to-end analytical pipelines in the past. Discuss the entire process from data ingestion to output, and be specific about the tools and technologies you used. This will show that you understand the full lifecycle of data processing.
✨Familiarise Yourself with Cloud Environments
Since this role involves cloud deployment, make sure you know your way around AWS, GCP, or Azure. Be prepared to discuss how you’ve deployed applications in these environments and how different components interact. This knowledge will set you apart from other candidates.
