At a Glance
- Tasks: Design and build high-performance data platforms for real-time trading decisions.
- Company: Leading global investment firm with a focus on innovation.
- Benefits: Excellent compensation, bonus potential, and long-term career progression.
- Other info: Join a dynamic team focused on modern cloud-native technologies.
- Why this job: Directly impact trading performance while collaborating with traders and researchers.
- Qualifications: 5+ years in Data Engineering with strong Python and SQL skills.
The predicted salary is between 80000 - 100000 £ per year.
Location
London
A leading global investment firm is looking for a Senior Data Engineer to join its Commodities Technology team.
Sitting at the intersection of front-office trading and enterprise data engineering, you\'ll design and build the data platforms that power real-time trading decisions.
This is a highly visible role where you\'ll work directly with traders, portfolio managers and quantitative researchers, owning business-critical data systems in a fast-paced, low-latency environment.
- What you\'ll be doing
- Design, build and maintain high-performance, low-latency data pipelines.
- Develop production-grade Python services and data engineering solutions.
- Own critical production systems, participating in an on-call rota and resolving live incidents.
- Work closely with front-office users to deliver reliable, high-quality data.
- Build and improve cloud-native, containerised data platforms.
- Drive engineering best practices through code reviews, testing and CI/CD.
- What we\'re looking for
- 5+ years\' experience in Data Engineering with ownership of production systems.
- Expert Python development for production services and bespoke data pipelines.
- Strong SQL skills with experience in complex data modelling and optimisation.
- Experience building real-time or near real-time data pipelines.
- Commercial experience with Kafka, Kubernetes and Docker.
- Strong understanding of Git, code reviews, CI/CD and testing practices.
- Experience supporting business-critical production systems.
- Exposure to Apache Airflow is beneficial.
- Commodities or energy market experience (power, gas, oil, agriculture or shipping) is advantageous but not essential.
Why apply?
- Work directly alongside traders and quantitative researchers.
- Build systems that have a direct impact on trading performance.
- Join a collaborative engineering team focused on modern cloud-native technologies.
- Take ownership of business-critical platforms in a highly technical environment.
- Excellent compensation, bonus potential and long-term career progression.
If you\'re an experienced Data Engineer looking to move into a high-performance front-office environment where your work directly influences trading, we\'d love to hear from you.
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Senior Engineer, Commodities Technology employer: Cross Atlantic Partners
Cross Atlantic Partners is an exceptional employer, offering a dynamic and collaborative work culture in the heart of London City. As a rapidly growing recruitment firm, we provide our employees with unique opportunities for career progression, exposure to high-profile investment firms, and the chance to work with cutting-edge AI technology. Join us to be part of a team that values ambition, innovation, and a supportive environment where your contributions truly matter.
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We think this is how you could land Senior Engineer, Commodities Technology
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We think you need these skills to ace Senior Engineer, Commodities Technology
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
