At a Glance
- Tasks: Create data products for traders and management to enhance decision-making.
- Company: Dynamic energy trading firm focused on innovation and collaboration.
- Benefits: Competitive salary up to £90k, flexible working, and career advancement opportunities.
- Other info: Opportunity for promotion to higher ranks, including principal level.
- Why this job: Join a cutting-edge team and shape the future of energy trading with your skills.
- Qualifications: Experience in data engineering or Python development, with a focus on stakeholder collaboration.
The predicted salary is between 81000 - 90000 £ per year.
Build the data products that front office traders and management use to make decisions.
This team runs front office data for an energy trading business.
You'll take market data and fundamental data, bring in internal data like positions, trades and Pn L, and turn it all into data products.
Sometimes that's a dataset, sometimes it's a visualisation.
Historically a lot of the visualisation work was done in Tableau.
More recently the team has moved to interactive tools like Plotly Dash and Streamlit.
So although the title says senior data engineer, this could also suit a python developer.
It's full stack in some respects, and the data engineering sits alongside that.
Stakeholders are a big part of the job.
You'll deal with risk, trading and management, usually in small, regular pieces of work, so you need to be comfortable talking to them and working out what they need.
You'll have a solid data focus, with some SQL or data engineering behind you.
It doesn't have to be your main strength, but it matters.
UI experience would be really nice to have, or if you've worked with the likes of React or Angular.
If you've worked in trading, 3-5 years is the sort of background that fits. Coming from outside trading, you'd need 5+ years and a history of building UIs.
There's scope to be promoted to a higher rank here, including up to principal.
Would you like to build front office data products in Python for a trading desk?
Data Engineer in London employer: Saragossa
Saragossa is an exceptional employer located in the vibrant city of London, offering a dynamic work culture that fosters innovation and collaboration. As a member of our team, you'll enjoy meaningful growth opportunities while working on cutting-edge ML-driven projects that directly impact our research platform. With a focus on real ownership and a clean-slate environment, we empower our employees to turn ideas into actionable strategies, making your contributions truly valuable.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer in London
✨Get Involved in Data Science Meetups
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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 Data Engineer at Saragossa.
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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 Saragossa.
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We think you need these skills to ace Data Engineer in London
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 Saragossa, 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 Saragossa. 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 Saragossa
✨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 Saragossa!
✨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.
