EDF Trading
Junior Data Engineer
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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 Group and our customers all over the world trust that their assets are managed by us in the most effective and efficient manner and are protected through expert risk management. Trading for over 20 years, it’s experience that makes us leaders in the field. Energy is what we do.
Become part of the team and you will be offered a great range of benefits, which include (location dependent) hybrid working, a personal pension plan, private medical and dental insurance, bi-annual health assessments, corporate gym memberships, an electric car lease programme, childcare vouchers, a cycle-to-work scheme, season ticket loans, volunteering opportunities, and much more.
Gender balance and inclusion are very high on the agenda at EDF Trading, so you will become part of an ever-diversifying family of around 750 colleagues based in London, Paris, Singapore, and Houston. Regular social and networking events, both physical and virtual, will ensure that you always feel connected to your colleagues and the business.
Who are we?
We are EDF Trading, part of the EDF Group - a world leader in low-carbon, sustainable electricity generation.
Join us, make a difference, and help shape the future of energy.
Job Description:
Data is Energy
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. Power production from renewables in response to weather, capacity limitations across borders, storage optimisation modelling... these are just some of the complex data opportunities we trade on every day.
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We’re looking for talented people who share our passion for data to join our team and seize these opportunities with us.
Team / department
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.
Required Skills and Experience
- 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.
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Desirable Skills and Experience
- 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.
Person Specification
- 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
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