At a Glance
- Tasks: Support the development of a modern data platform and collaborate with the Data team.
- Company: Join EDF Trading, a leader in low-carbon energy solutions with a diverse international team.
- Benefits: Enjoy hybrid working, private medical insurance, gym memberships, and more.
- Other info: Dynamic environment with opportunities for growth and continuous learning.
- Why this job: Be part of a data-driven revolution in the energy sector and make a real impact.
- Qualifications: Basic understanding of data engineering concepts and SQL skills required.
The predicted salary is between 30000 - 40000 £ per year.
When you join EDF Trading, you’ll become part of a diverse international team of experts who challenge conventional ideas, test new approaches, and think outside the box. Energy markets evolve rapidly, so our team needs to remain agile, flexible, and ready to spot opportunities across all the markets we trade in power, gas, LNG, LPG, oil, and environmental products.
EDF Trading is a data business. Trading is transitioning into a data driven business. High quality data and the agility of the analysis are becoming the differentiator. EDF Trading has a leading footprint in the European energy markets and wants to monetise and optimise data as an asset. The European energy space is complex and has a huge appetite for data.
The Data team is responsible for providing business solutions aimed at extracting value from large amounts of data. It covers a broad range of activities such as collecting market data and building related analysis tools, processing of real-time data streams, data governance and data science. The role will focus on building the foundational data platform that will enable all other Data services.
Main responsibilities:- Support the implementation of a modern data platform by collaborating with the broader Data team and contributing to the development of data lakehouse solutions aligned with company needs.
- Assist in building and maintaining data storage solutions for different use cases, following established standards (e.g., Parquet, Apache Iceberg) and guidance from senior team members.
- Develop and maintain data ingestion and transformation workflows using standard tools and frameworks, ensuring basic logging and monitoring practices are followed.
- Help capture and maintain data lineage for ingestion and transformation processes using approved tools and processes.
- Contribute to data quality efforts by implementing validation checks, schema enforcement, and basic error handling within pipelines.
- Collaborate with analytics and business teams to understand data requirements and support data modelling activities.
- Follow established governance and security standards, including access control and data handling best practices.
- Stay up to date with data engineering tools and technologies and proactively build technical skills.
- Participate in agile ceremonies (e.g., stand-ups, sprint planning, retrospectives) as part of the delivery team.
- Work with senior engineers to troubleshoot data issues and support platform monitoring and maintenance.
- Foundational Data Concepts: Basic understanding of data engineering concepts such as ETL/ELT pipelines, data modeling, and data storage formats.
- SQL Skills: Working knowledge of SQL for querying and manipulating data.
- Programming: Beginner to intermediate experience with Python, SQL, or similar languages used in data processing.
- Data Processing Tools: Exposure to tools such as Apache Spark, dbt, or similar (academic or project experience acceptable).
- Version Control: Familiarity with Git and basic collaborative development workflows.
- Cloud & Storage Concepts: Basic understanding of cloud platforms (e.g., Azure, AWS, GCP) and object storage concepts.
- Problem-Solving & Learning Mindset: Strong willingness to learn, troubleshoot, and grow in a fast-paced environment.
- Exposure to data lakehouse concepts or modern data platforms (coursework or projects is sufficient).
- Familiarity with orchestration tools such as Airflow or Dagster.
- Awareness of distributed data processing frameworks.
- Basic knowledge of data governance, quality, and security principles.
- Experience with vendor platforms such as Databricks or Microsoft Fabric (even at a basic level).
- Understanding of streaming concepts (e.g., Kafka) is a plus.
- Familiarity with monitoring and debugging data pipelines.
- Positive and proactive attitude, with a willingness to learn and take on new challenges.
- Curiosity and eagerness to understand problems, with the ability to break them down and ask the right questions.
- Interest in building reliable and high-quality data solutions, with attention to detail.
- Good communication skills, with the ability to collaborate effectively and clearly share ideas within the team.
- Team-oriented mindset, comfortable working in a collaborative, multi-disciplinary environment.
- Adaptability and openness to feedback, using guidance from senior team members to improve and grow.
- Problem-solving mindset, with persistence in troubleshooting and resolving issues.
- Interest in continuous learning, staying up to date with data engineering tools and best practices.
Hours of work: 8.30am – 5.30pm Monday to Friday. Hybrid working arrangement.
Junior Data Engineer in London employer: EDFT
At EDF Trading, we pride ourselves on being an exceptional employer that fosters a dynamic and inclusive work culture. Our London office offers a wealth of benefits, including hybrid working options, comprehensive health plans, and opportunities for personal and professional growth, all while being part of a diverse team that values innovation and collaboration. Join us to not only advance your career in international tax but also contribute to shaping the future of sustainable energy.
StudySmarter Expert Advice🤫
We think this is how you could land Junior Data Engineer in London
✨Get Involved in Data Science Meetups
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✨Show Off Your Projects
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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 EDFT.
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We think you need these skills to ace Junior 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 EDFT, 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 EDFT. 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 EDFT
✨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 EDFT!
✨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.
