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- Tasks: Engineer real-time data feeds and optimise data pipelines for energy trading.
- Company: Leading global energy commodities trading firm with a focus on innovation.
- Benefits: Competitive salary, bonuses, comprehensive insurance, and tuition assistance.
- Other info: Opportunities for charity volunteering and continued learning.
- Why this job: Make a direct impact in a fast-paced, data-driven trading environment.
- Qualifications: 5-10 years as a data engineer with experience in energy markets.
The predicted salary is between 70000 - 90000 £ per year.
This is an energy trading and infrastructure asset investment firm, powered by deep fundamental research and advanced analytics. Now seeking a skilled senior data engineer to support the Intraday Short-Term Power business within the Data Science & Technology team in London. This team focuses on engineering the availability, quality and performance of real-time data feeds that drive execution strategies, analytics and decision-making. You will integrate and manage feeds from multiple sources, including fundamental market data, grid operations, weather providers and internal trading systems. This role offers the opportunity to work in a fast-paced, data-driven trading environment, making a direct impact on systematic trading and risk management.
Responsibilities
- Engineer the availability, quality and performance of real-time data feeds for the Intraday Short-Term Power business.
- Integrate and manage feeds from multiple sources: fundamental market data, grid operations, weather providers and internal trading systems.
- Develop and optimize data pipelines (ETL/ELT) to support execution strategies and analytics.
Qualifications
- 5-10 years as a data engineer, ideally in Intraday Short-Term Power trading or similar real-time energy market environments.
- Experience with energy commodity time-series datasets.
- Understanding of systematic trading workflows (signal generation, back-testing, model validation).
- Ability to work in a high-frequency, intraday trading environment with tight feedback loops.
- ETL/ELT frameworks experience writing pipelines to load millions/billions of records.
- Advanced skills in writing highly optimized SQL code and relational databases.
- Hands-on experience developing data solutions in Python, Pandas, NumPy, etc.
Desirable
- Exposure to AWS and Snowflake technologies.
- Familiarity with short-term power market data sources such as EPEX, ENTSO-E or Nord Pool.
Rewards and Incentives
- Competitive base salaries plus bonuses.
- Generous benefits program, including:
- Parental and family leave policies.
- Comprehensive insurance (medical, dental, retirement, life).
- Charity volunteering allowance and charitable contribution match program.
- Tuition assistance and reimbursement plus continued learning opportunities.
Senior Data Engineer (Short-Term Power Markets)- Leading Global Energy Commodities Trading in London employer: Jobs via eFinancialCareers
Contact Detail:
Jobs via eFinancialCareers Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer (Short-Term Power Markets)- Leading Global Energy Commodities Trading in London
✨Tip Number 1
Network like a pro! Reach out to folks in the energy trading space, especially those who work with data. LinkedIn is your best mate here – connect, engage, and don’t be shy to ask for a chat about their experiences.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio of projects or GitHub repositories showcasing your data engineering prowess, make sure to highlight them during interviews. Real-world examples can set you apart from the crowd.
✨Tip Number 3
Prepare for technical challenges! Brush up on your SQL and Python skills, and be ready to tackle some coding problems on the spot. Practising common data engineering scenarios will help you feel more confident.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Senior Data Engineer (Short-Term Power Markets)- Leading Global Energy Commodities Trading in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data engineering, especially in real-time energy markets. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects and achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about the energy sector and how your background makes you a perfect fit for our team. Keep it concise but impactful!
Showcase Technical Skills: We’re looking for advanced SQL skills and experience with Python, Pandas, and ETL/ELT frameworks. Make sure to mention specific projects where you’ve used these technologies to solve real-world problems.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Jobs via eFinancialCareers
✨Know Your Data Inside Out
Make sure you’re well-versed in the specific data sources mentioned in the job description, like EPEX and ENTSO-E. Brush up on your understanding of energy commodity time-series datasets and how they impact trading strategies.
✨Showcase Your Technical Skills
Be ready to discuss your experience with ETL/ELT frameworks and SQL. Prepare examples of how you've optimised data pipelines in previous roles, especially in high-frequency trading environments. This will demonstrate your hands-on expertise.
✨Understand Systematic Trading Workflows
Familiarise yourself with the systematic trading processes, including signal generation and model validation. Being able to articulate how your work as a data engineer supports these workflows will set you apart from other candidates.
✨Prepare for Real-Time Scenarios
Since this role involves working in a fast-paced environment, think of scenarios where you had to make quick decisions based on real-time data. Be prepared to discuss how you handled tight feedback loops and what impact your decisions had on trading outcomes.