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For Companies At a Glance
- Tasks: Build and maintain data infrastructure for trading strategies in a fast-paced environment.
- Company: Join a leading alternative asset manager focused on systematic investing.
- Benefits: Enjoy competitive salary, bonuses, private healthcare, and 25 days annual leave.
- Why this job: Gain invaluable experience in a high-talent team with excellent career progression opportunities.
- Qualifications: Strong STEM background, Python expertise, and experience with ETL pipelines and SQL required.
- Other info: Work closely with quantitative researchers to tackle complex data challenges.
The predicted salary is between 43200 - 72000 £ per year.
The Client
My client is a market leading alternative asset manager focused on multi-asset systematic investing. They are looking for a Python focused Data Engineer to join their quantitative platform team.
What You'll Get
- An opportunity to work in one of the most exciting and fast growing buy-side businesses in the City.
- An opportunity to join a strong team with a very high talent density presenting lots of opportunity for learning and development.
- Incredible career progression opportunities with potential access to all areas of the business.
- A market leading compensation package including basic salary and annual bonus.
- Benefits including a pension, private healthcare, life assurance and 25 days annual leave.
What You'll Do
- Build and maintain the data infrastructure that fuels the funds research and trading strategies.
- Take responsibility for the end-to-end lifecycle of diverse datasets – including market, fundamental, and alternative sources – ensuring their timely acquisition, rigorous cleaning and validation, efficient storage, and reliable delivery through robust data pipelines.
- Work closely with quantitative researchers and technologists to tackle complex challenges in data quality, normalisation, and accessibility, ultimately providing the high-fidelity, readily available data essential for developing and executing sophisticated investment models in a fast-paced environment.
What You'll Need
- Strong academic background in a STEM or Computer Science focused discipline.
- Strong Python engineering experience.
- Experience building ETL pipelines using Python.
- Experience of SQL and relational databases.
- Experience with AWS or similar Cloud technology.
- Experience with S3, Kafka, Airflow, and Iceberg will be beneficial.
- Experience in the financial markets with a focus on securities & derivatives trading.
- Exceptional communication skills, attention to detail, and adaptability.
Python Data Engineer - Systematic Trading - Hedge Fund employer: Tempest Vane Partners
Contact Detail:
Tempest Vane Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Python Data Engineer - Systematic Trading - Hedge Fund
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as AWS, Kafka, and Airflow. Having hands-on experience or projects that showcase your skills with these tools can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the finance and data engineering sectors. Attend industry meetups or webinars to connect with people who work in hedge funds or systematic trading, as they might provide insights or even referrals for the position.
✨Tip Number 3
Prepare to discuss your experience with building ETL pipelines in detail. Be ready to explain the challenges you faced, how you overcame them, and the impact your work had on previous projects, as this will demonstrate your problem-solving skills.
✨Tip Number 4
Showcase your understanding of the financial markets, particularly in securities and derivatives trading. Being able to speak knowledgeably about market trends and how data influences trading strategies will impress interviewers and show your genuine interest in the role.
We think you need these skills to ace Python Data Engineer - Systematic Trading - Hedge Fund
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python engineering experience and any relevant projects or roles that demonstrate your skills in building ETL pipelines. Mention your familiarity with SQL, AWS, and any other technologies listed in the job description.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your background in STEM or Computer Science aligns with their needs, and provide specific examples of how you've tackled challenges in data quality and accessibility.
Showcase Relevant Projects: If you have worked on projects involving data infrastructure, market data handling, or financial models, be sure to include these in your application. Highlight your role, the technologies used, and the impact of your work.
Proofread Your Application: Before submitting, carefully proofread your CV and cover letter for any errors or typos. Ensure that your communication is clear and professional, as exceptional communication skills are a key requirement for this position.
How to prepare for a job interview at Tempest Vane Partners
✨Showcase Your Python Skills
Make sure to highlight your Python engineering experience during the interview. Be prepared to discuss specific projects where you've built ETL pipelines and how you tackled challenges in data processing.
✨Demonstrate Your Understanding of Data Infrastructure
Discuss your experience with data lifecycle management, including acquisition, cleaning, validation, and storage. Providing examples of how you've ensured data quality and accessibility will impress the interviewers.
✨Familiarise Yourself with Relevant Technologies
Brush up on your knowledge of AWS, S3, Kafka, Airflow, and Iceberg. Being able to speak confidently about these technologies and how you've used them in past roles will show that you're well-prepared for the position.
✨Communicate Effectively
Exceptional communication skills are crucial for this role. Practice articulating complex technical concepts clearly and concisely, especially when discussing your collaboration with quantitative researchers and technologists.
