At a Glance
- Tasks: Design and maintain scalable data pipelines using Scala and Quantexa for advanced analytics.
- Company: Join a leading banking client in the heart of London with a hybrid working model.
- Benefits: Enjoy competitive pay, flexible work, and opportunities for professional growth.
- Other info: Collaborate with data scientists and analysts in a fast-paced financial services environment.
- Why this job: Make an impact by enabling insights from complex datasets in a dynamic team.
- Qualifications: Experience in data engineering, Scala/Java, and familiarity with graph analytics.
The predicted salary is between 60000 - 80000 £ per year.
Hybrid Working - London - 1-2 days a week on site.
Financial Services
Lorien's leading banking client is looking for a Data Engineer to join a newly built team on a new project. The Ideal Candidate will design, build, and maintain scalable data pipelines and analytical solutions with a strong emphasis on Scala-based data processing, graph analytics, advanced data wrangling and experienced in using and understanding Quantexa. You will work closely with data scientists, analysts, and platform engineers to enable insights from complex, interconnected datasets. This role is based in London and will be via umbrella, working in a hybrid model of 1-2 days a week on site.
Key Skills and Experience- Experienced in design, develop, and maintain scalable data pipelines using Scala/Java and modern distributed data processing frameworks.
- Build and optimize graph-based data models to analyse relationships, networks, and dependencies across large datasets.
- Experienced with using and working with Quantexa is highly advantageous.
- Implement graph analytics algorithms (e.g. Centrality, Community Detection, Path Analysis) to support advanced analytical use cases.
- Perform complex data wrangling and transformation on structured and semi-structured data.
- Develop and maintain scripts (e.g. Scala) to automate data ingestion, validation, and processing tasks.
- Ensure data quality, reliability, and performance across data workflows.
- Collaborate with stakeholders to translate business requirements into robust data solutions.
- Contribute to data architecture, best practices, and documentation.
- Monitor and troubleshoot data pipelines in production environments.
- Experience of working within financial services environments.
Data Engineer - Quantexa. Job in London Move Collective Jobs employer: Broughton Group
Join a dynamic and innovative team at our London-based financial services client, where you will have the opportunity to work on cutting-edge data engineering projects in a hybrid working environment. We prioritise employee growth through continuous learning and collaboration with talented professionals, fostering a culture of inclusivity and support. Enjoy the unique advantage of being part of a newly built team, allowing you to make a significant impact while enjoying the vibrant atmosphere of London.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Quantexa. Job in London Move Collective Jobs
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at Quantexa or similar companies. A friendly chat can open doors and give you insider info that could make all the difference.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio of projects or GitHub repos showcasing your Scala and data pipeline work, make sure to highlight them during interviews. It’s a great way to demonstrate your hands-on experience.
✨Tip Number 3
Prepare for technical questions! Brush up on graph analytics algorithms and be ready to discuss how you’ve implemented them in past projects. This will show you’re not just familiar with the concepts but can apply them effectively.
✨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 take that extra step to connect with us directly.
We think you need these skills to ace Data Engineer - Quantexa. Job in London Move Collective Jobs
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Engineer role. Highlight your experience with Scala, data pipelines, and any work you've done with Quantexa. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how you can contribute to our team. Keep it concise but impactful – we love a good story!
Showcase Relevant Projects:If you've worked on any projects that involved graph analytics or complex data wrangling, make sure to mention them. We’re keen to see real examples of your work and how you’ve tackled challenges in the past.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep everything organised and ensures your application gets the attention it deserves. Don’t miss out!
How to prepare for a job interview at Broughton Group
✨Know Your Scala and Quantexa
Make sure you brush up on your Scala skills and understand how Quantexa works. Be ready to discuss specific projects where you've designed or maintained data pipelines using these technologies. Showing that you can talk the talk will impress the interviewers.
✨Showcase Your Data Wrangling Skills
Prepare examples of how you've performed complex data wrangling and transformation. Think about structured and semi-structured data you've worked with, and be ready to explain your approach to ensuring data quality and reliability in your previous roles.
✨Understand Graph Analytics
Familiarise yourself with graph analytics algorithms like Centrality and Community Detection. Be prepared to discuss how you've implemented these in past projects, as this will demonstrate your ability to analyse relationships and dependencies within large datasets.
✨Collaboration is Key
Since you'll be working closely with data scientists and analysts, think of examples where you've collaborated effectively with stakeholders. Highlight how you translated business requirements into data solutions, as this shows your ability to work in a team and contribute to the overall project success.
