At a Glance
- Tasks: Build and evolve a cloud-native data platform for systematic trading.
- Company: Leading global investment firm focused on technology and innovation.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Collaborative environment with exposure to cutting-edge data technologies.
- Why this job: Join a dynamic team and make an impact in the finance tech space.
- Qualifications: 5+ years in data engineering with strong Python and SQL skills.
The predicted salary is between 70000 - 90000 € per year.
The client, a leading global investment firm with a strong focus on technology and systematic strategies, is seeking an experienced Data Engineer to join their expanding London office. The role is a Senior Data Engineer set to join a team responsible for building a research-facing data platform that supports systematic trading and electronic market-making. The platform is designed to make market and reference data consistent, traceable, and easy to use at scale while working directly with Quants and Traders.
- Build and evolve a cloud-native data platform supporting research and trading workflows
- Develop and maintain Python-first data pipelines and access layers for large-scale datasets
- Design and standardise datasets across asset classes, ensuring consistency and usability
- Implement and improve data lineage, freshness tracking, and metadata systems
- Work with columnar data processing tools (e.g. PyArrow, Polars) for efficient data handling
- Collaborate closely with Quants and Researchers to improve how data is consumed and used
- Continuously enhance a pragmatic, production data environment, balancing performance, reliability, and maintainability
- Take ownership of systems end-to-end, from ingestion through to consumption
Key Requirements
- 5+ years' experience building data platforms or data infrastructure
- Strong Python and SQL skills (Python-first engineering environment)
- Experience with data modelling, schema design, and dataset standardisation
- Hands-on experience with cloud environments (ideally AWS)
- Experience working with large-scale, real-world datasets in production
- Familiarity with workflow orchestration tools (e.g. Airflow)
- Exposure to columnar data processing (e.g. PyArrow, Polars, Spark)
Senior Data Engineer employer: Selby Jennings
Join a leading global investment firm in London that prioritises technology and innovation, offering a dynamic work culture where collaboration with Quants and Traders is at the forefront. As a Senior Data Engineer, you'll benefit from a strong focus on employee growth, with opportunities to enhance your skills in a cloud-native environment while contributing to impactful projects that shape systematic trading strategies. Enjoy a competitive benefits package and a vibrant office atmosphere that fosters creativity and professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the investment and tech sectors. Attend meetups or webinars where you can chat with industry folks. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving Python and cloud platforms. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your experience with data pipelines and cloud environments. Practise common interview questions related to data engineering to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you. Plus, applying directly shows your enthusiasm and commitment to joining our team at StudySmarter.
We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Senior Data Engineer role. Highlight your Python and SQL expertise, as well as any experience with cloud environments and data modelling. We want to see how you can contribute to our team!
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 your background aligns with our mission at StudySmarter. Don’t forget to mention your experience with large-scale datasets and collaboration with Quants and Researchers.
Showcase Your Projects:If you've worked on relevant projects, make sure to include them in your application. Whether it's building data pipelines or working with columnar data processing tools, we love seeing real-world examples of your work. It helps us understand your hands-on experience!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It streamlines the process and ensures your application lands in the right hands. Plus, it shows you're keen on joining our awesome team at StudySmarter!
How to prepare for a job interview at Selby Jennings
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python and SQL. Brush up on your experience with cloud environments like AWS and be ready to discuss how you've used these tools in past projects.
✨Showcase Your Data Pipeline Skills
Prepare to talk about your experience building data pipelines. Have specific examples ready that demonstrate your ability to design and standardise datasets, as well as how you’ve implemented data lineage and freshness tracking in previous roles.
✨Collaborate Like a Pro
Since this role involves working closely with Quants and Researchers, think of examples where you’ve successfully collaborated with cross-functional teams. Highlight how you’ve improved data consumption and usability in those situations.
✨Be Ready for Technical Questions
Expect technical questions related to columnar data processing tools like PyArrow or Polars. Brush up on your knowledge of these tools and be prepared to explain how they can enhance data handling efficiency in a production environment.
