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- Tasks: Build and maintain data pipelines for trading and research in a dynamic financial environment.
- Company: Leading financial data firm based in London with a focus on innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Why this job: Join a team that drives impactful decisions through data and technology.
- Qualifications: Experience with ETL/ELT pipelines in Python and strong communication skills.
- Other info: Collaborative culture with a focus on data-driven solutions.
The predicted salary is between 36000 - 60000 £ per year.
A financial data firm in London seeks a Data Engineer to build and maintain essential data infrastructure for research and trading. This role involves managing diverse datasets and optimizing data pipelines while collaborating with quantitative researchers.
The ideal candidate has experience with ETL/ELT pipelines in Python, strong communication skills, and a passion for operational roles within a data-driven environment.
Quantitative Data Engineer: Build Trading Data Pipelines in City of London employer: Winton
Contact Detail:
Winton Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Data Engineer: Build Trading Data Pipelines in City of London
✨Tip Number 1
Network like a pro! Reach out to professionals in the financial data sector on LinkedIn or at industry events. Building connections can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with ETL/ELT pipelines in Python. This will give potential employers a taste of what you can do and set you apart from the competition.
✨Tip Number 3
Prepare for interviews by brushing up on your communication skills. Practice explaining complex data concepts in simple terms, as collaboration with quantitative researchers is key in this role.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Quantitative Data Engineer: Build Trading Data Pipelines in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with ETL/ELT pipelines in Python. 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 data engineering and how you can contribute to our team. Keep it concise but engaging – we love a good story!
Show Off Your Communication Skills: Since collaboration is key in this role, make sure to highlight your strong communication skills in your application. We want to know how you’ve worked with others in the past and how you can bring that to our team.
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 the role. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at Winton
✨Know Your Data Pipelines
Make sure you can discuss your experience with ETL/ELT pipelines in Python confidently. Be ready to explain how you've built and optimised data pipelines in previous roles, as this will show your technical expertise and problem-solving skills.
✨Brush Up on Communication Skills
Since collaboration with quantitative researchers is key, practice explaining complex data concepts in simple terms. This will demonstrate your ability to communicate effectively and work well in a team environment.
✨Research the Company
Familiarise yourself with the financial data firm’s projects and values. Understanding their focus areas will help you tailor your answers and show genuine interest in the role, making you stand out as a candidate.
✨Prepare for Technical Questions
Expect technical questions related to data management and pipeline optimisation. Review common challenges faced in data engineering and think of examples from your past experiences that showcase your skills in overcoming these challenges.
