KDB+/Q Engineer: Real-Time Market Analytics & Trading

KDB+/Q Engineer: Real-Time Market Analytics & Trading

Full-Time 60000 - 80000 £ / year (est.) No working from home possible
Hunter Bond

At a Glance

  • Tasks: Build next-gen research and trading platforms using KDB+/q in a high-performance setting.
  • Company: Join Hunter Bond, a leading hedge fund with a focus on innovation.
  • Benefits: Competitive salary, dynamic work environment, and opportunities for professional growth.
  • Other info: Fast-paced, globally distributed team with exciting challenges.
  • Why this job: Make an impact by optimising real-time market data applications in finance.
  • Qualifications: Experience with KDB+/q and a passion for analytics and trading.

The predicted salary is between 60000 - 80000 £ per year.

Hunter Bond is seeking a KDB+/q Developer to build next-generation research, analytics, and trading platforms in a high-performance hedge fund setting.

You will design, optimize, and scale real-time market data applications and collaborate with quants, portfolio managers, and traders to deploy production-ready solutions.

Take ownership of critical systems and contribute to a fast-paced, globally distributed research and trading environment.

#J-18808-Ljbffr

KDB+/Q Engineer: Real-Time Market Analytics & Trading employer: Hunter Bond

Hunter Bond is an exceptional employer, offering a dynamic work environment in the heart of London where innovation meets collaboration. With a strong focus on employee growth and development, team members benefit from competitive compensation, comprehensive benefits, and the opportunity to work alongside a talented, low-ego team dedicated to pushing the boundaries of technology in trading systems.

Hunter Bond

Contact Details:

Hunter Bond Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land KDB+/Q Engineer: Real-Time Market Analytics & Trading

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Hunter Bond!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like KDB+/Q Engineer: Real-Time Market Analytics & Trading at Hunter Bond.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Hunter Bond.

Apply Directly through Our Website

When you find a suitable opening like KDB+/Q Engineer: Real-Time Market Analytics & Trading at Hunter Bond, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace KDB+/Q Engineer: Real-Time Market Analytics & Trading

KDB+/q Development
Real-Time Market Data Applications
System Design
Performance Optimisation
Scalability
Collaboration with Quants
Collaboration with Portfolio Managers

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!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Hunter Bond, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Hunter Bond. 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!

How to prepare for a job interview at Hunter Bond

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!

Showcase Your Projects

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

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Hunter Bond!

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.