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- Tasks: Build and support data pipelines while designing robust trading tools.
- Company: Leading global financial services firm in London with a focus on diversity.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Why this job: Make a substantial impact in the financial services industry with cutting-edge technology.
- Qualifications: Strong expertise in Python and experience in quant trading and data processing.
- Other info: Join a dynamic team committed to innovation and inclusion.
The predicted salary is between 48000 - 84000 £ per year.
A leading global financial services firm in London is seeking a Sr Lead Software Engineer to enhance technology products within their Equities Electronic Trading team. The successful candidate will focus on building and supporting data pipelines and designing robust trading tools. Strong expertise in Python and experience with quant trading and data processing are required. Join a firm committed to diversity and inclusion to make a substantial impact in the financial services industry.
VP Quant Data Engineer: Python for Systematic Trading employer: JPMorganChase
Contact Detail:
JPMorganChase Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land VP Quant Data Engineer: Python for Systematic Trading
✨Tip Number 1
Network like a pro! Reach out to folks in the finance and tech sectors, especially those who work in quant trading. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio of projects or contributions to open-source, make sure to highlight them. It’s a great way to demonstrate your Python prowess and data processing know-how.
✨Tip Number 3
Prepare for the interview by brushing up on your quant trading knowledge. Be ready to discuss how you can enhance trading tools and data pipelines. We want to see your passion for the industry!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace VP Quant Data Engineer: Python for Systematic Trading
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python and quant trading. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about systematic trading and how you can contribute to our Equities Electronic Trading team. Keep it engaging and personal!
Showcase Your Problem-Solving Skills: In your application, give examples of how you've tackled challenges in data processing or trading tools. We love seeing candidates who can think critically and innovate solutions!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity in our diverse and inclusive team!
How to prepare for a job interview at JPMorganChase
✨Know Your Python Inside Out
Make sure you brush up on your Python skills, especially as they relate to data processing and quant trading. Be prepared to discuss specific projects where you've used Python to build or enhance data pipelines.
✨Understand the Trading Landscape
Familiarise yourself with the current trends in electronic trading and how data plays a role in decision-making. Being able to discuss recent developments in the industry will show your passion and knowledge.
✨Showcase Your Problem-Solving Skills
Prepare to tackle hypothetical scenarios during the interview. Think about how you would approach building a trading tool or optimising a data pipeline, and be ready to explain your thought process clearly.
✨Emphasise Diversity and Inclusion
Since the firm values diversity and inclusion, be ready to share your thoughts on how diverse teams can drive innovation. Highlight any experiences you've had working in diverse environments and how it has shaped your approach to teamwork.
