At a Glance
- Tasks: Design and maintain complex Python infrastructure for quant trading.
- Company: Leading HFT firm with a start-up feel and established benefits.
- Benefits: Competitive salary, bonus, flexible WFH, and free meals.
- Other info: Collaborative culture with opportunities for ownership and growth.
- Why this job: Join a dynamic team and make an impact in automated electronic trading.
- Qualifications: Solid Python and Linux knowledge; financial experience not required.
The positive feel of a start-up with the benefits that come with a more established player, this leading HFT firm is looking for a dynamic software engineer to join one of their most successful quant trading teams. Collaborating extensively with traders and other technologists, you'll design, write and maintain a complex Python infrastructure. This role is as front office as developers come within the firm without being a Quant.
The ideal candidate will be passionate about:
- data
- development
- keen to learn about automated electronic trading
Financial experience is not essential; Solid knowledge of Python and Linux (some C++ preferable); able to take ownership of several projects at once; Knowledge of SQL, Redis, InfluxDB is a bonus; Bachelor's degree (or higher) in Computer Science or Computer Engineering (or equivalent).
Competitive base salary + bonus; they will pay leading market rate / are flexible for the right candidate. They're willing to be flexible with WFH. Collaborative and rewarding work environment and culture. Free breakfast, lunch and dinner.
Whilst we carefully review all applications, to all jobs, due to the high volume of applications we receive it is not possible to respond to those who have not been successful.
Data Engineer Lead in London employer: Oxford Knight
As one of the world's leading algorithmic trading firms, we offer an exceptional work environment where innovation thrives. Our friendly and informal culture fosters collaboration across teams, allowing you to tackle complex challenges with cutting-edge technologies while enjoying a market-leading salary and generous benefits. Join us to make a significant impact in the financial sector and advance your career in a role that values your contributions and expertise.
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We think this is how you could land Data Engineer Lead in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Data Engineer Lead in London
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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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Oxford Knight. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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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.
