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- Tasks: Join a dynamic team to manage data ingestion, storage, and transformation.
- Company: A leading Quantitative Hedge Fund known for its innovative approach and supportive culture.
- Benefits: Enjoy hybrid working, a friendly atmosphere, and opportunities for professional growth.
- Why this job: Be part of a cutting-edge team revamping data processes and enhancing strategies.
- Qualifications: 1-3 years in finance or tech, with skills in Python, SQL, and data pipelines.
- Other info: Work in a collaborative environment with low staff turnover and strong team spirit.
The predicted salary is between 36000 - 60000 £ per year.
Data Engineer with 1-3 years experience gained in a Hedge Fund, Investment Bank, FinTech or similar sought to join a market-leading Quantitative Hedge Fund. You will join a 5-strong Data Engineering team covering the ingestion, storage, transformation and distribution of tick, timeseries, reference and alternative datasets.
The technology stack is varied including a range of legacy and modern systems, across on-premises and cloud infrastructure with technologies and tooling such as Python, dbt, KDB+, Snowflake, SQL and interfacing with Market Data vendors such as Bloomberg, Refinitiv, Factset and MorningStar.
This is an exciting time for you to join the team as they consolidate their technology estate, revamp how they process and filter data, and overhaul the way data is accessed by their consumers, while continuing to onboard new datasets that enhance their strategies. They are a lean team owning end-to-end delivery from initial design through to operational support in production.
The firm works on a hybrid working schedule, with a minimum of three days per week in the office, and are renowned for their friendly, supportive and collegiate culture, with an enviably low staff turnover.
Requirements:- 1-3+ years experience gained in a Hedge Fund, Investment Bank, FinTech or similar
- Expertise in Python and SQL and familiarity with relational and time-series databases
- Exposure to Airflow and dbt, as well as Snowflake, Databricks or other Cloud Data Warehouses preferred
- Experience implementing data pipelines from major financial market data vendors (Bloomberg, Refinitiv, Factset…)
- SDLC and DevOps: Git, Docker, Jenkins/TeamCity, monitoring, testing, agile practices
- Passionate about code quality, data integrity, and building scalable and robust systems
- Ability to communicate clearly with technical and non-technical colleagues
This is an incredible opportunity for a Data Engineer with 1-3 years of experience to join a market-leading Quantitative Hedge Fund.
Contact Detail:
Winston Fox Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer – Quant Hedge Fund (City of London)
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as Python, SQL, and Snowflake. Having hands-on experience or projects that showcase your skills in these areas will make you stand out during discussions.
✨Tip Number 2
Network with professionals in the hedge fund and fintech sectors. Attend industry meetups or webinars to connect with current employees or recruiters from similar firms. This can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Prepare to discuss your experience with data pipelines and market data vendors like Bloomberg and Refinitiv. Be ready to share specific examples of how you've implemented solutions or improved processes in previous roles.
✨Tip Number 4
Showcase your passion for code quality and data integrity by discussing any relevant personal projects or contributions to open-source initiatives. This demonstrates your commitment to building robust systems and can set you apart from other candidates.
We think you need these skills to ace Data Engineer – Quant Hedge Fund (City of London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data engineering, particularly within hedge funds or similar environments. Emphasise your expertise in Python, SQL, and any relevant technologies like dbt or Snowflake.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data engineering and your understanding of the financial sector. Mention specific projects or experiences that align with the job description, especially those involving data pipelines and market data vendors.
Highlight Relevant Skills: In your application, clearly outline your technical skills, including your familiarity with tools like Airflow, Git, and Docker. Provide examples of how you've used these tools in past roles to demonstrate your capability.
Showcase Communication Skills: Since the role requires clear communication with both technical and non-technical colleagues, include examples in your application where you've successfully communicated complex ideas or collaborated with diverse teams.
How to prepare for a job interview at Winston Fox
✨Showcase Your Technical Skills
Make sure to highlight your expertise in Python and SQL during the interview. Be prepared to discuss specific projects where you've implemented data pipelines or worked with time-series databases, as this will demonstrate your hands-on experience.
✨Familiarise Yourself with the Tech Stack
Research the technologies mentioned in the job description, such as dbt, Snowflake, and Airflow. Being able to discuss how you've used these tools or similar ones in past roles will show that you're well-prepared and genuinely interested in the position.
✨Communicate Clearly
Since the role requires interaction with both technical and non-technical colleagues, practice explaining complex concepts in simple terms. This will help you convey your ideas effectively and demonstrate your communication skills.
✨Emphasise Team Collaboration
Given the firm's friendly and supportive culture, be ready to share examples of how you've successfully collaborated within a team. Highlight any experiences where you contributed to a project from design to operational support, showcasing your end-to-end delivery capabilities.
