At a Glance
- Tasks: Bridge the gap between traders and data engineers, managing the full data life cycle.
- Company: Join a leading alternative investment manager in a dynamic London office.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Unique role with high visibility in a successful business environment.
- Why this job: Make a real impact by translating trading strategies into scalable data solutions.
- Qualifications: 10+ years experience, strong SQL & Python skills, and stakeholder management expertise.
The predicted salary is between 80000 - 100000 £ per year.
We have partnered with a highly successful alternative investment manager on a very exciting hybrid position where you will be given the opportunity to capitalise on both your commercial acumen and stakeholder management skills alongside your technical background & expertise. Sitting in their London office, you will be bridging the gap between the traders and the data engineering team to support the scaling data infrastructure of the firm.
Key responsibilities:
- Operate across the full data life cycle - from requirement gathering through delivery
- Gather and clarify business requests into actionable deliverables, partnering closely with the data engineering team to support implementation and delivery
- Ensure data products are implemented in line with architectural standards and principles
- Utilise market data and alternative datasets across multiple asset classes to translate complex trading strategies into scalable data engineering requirements, ensuring seamless delivery to the front office
Your background:
- 10+ years experience in a similar role
- Deep understanding of schemas, data modelling principles, query optimisation - working understanding of SQL & Python will be important
- Demonstrated experience in stakeholder management with front office - comfortable acting as the bridge between developers/engineers & traders
- Extensive experience working with market data/alternative datasets across multiple asset classes (ideally with vendors like Bloomberg or Refinitiv/LSEG), experience working across structured & unstructured datasets
- Understanding data normalization and de-normalization challenges when integrating multiple vendors and internal systems
- Experience with equities & fixed-income asset classes (buy or sell-side)
This role is not suitable for someone who is strictly technical or engineering-only focused, nor is it relevant for a pure functional/delivery-focused Business Analyst. This is a hybrid role, where you will need to utilise your technical background in order to sit with traders, understand their priorities, and feed this back to our data engineers. This is a very unique opportunity to sit in a high-impact role in a hugely successful business.
StudySmarter Expert Advice🤫
We think this is how you could land Business Data Analyst in Slough
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We think you need these skills to ace Business Data Analyst in Slough
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!
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Craft a Tailored Cover Letter:For a full-time role at Goodman Masson, 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.
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How to prepare for a job interview at Goodman Masson
✨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!
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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
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✨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.
