> Markdown version of [/jobs/ext/1445140-junior-data-engineer](https://www.wearedevelopers.com/jobs/ext/1445140-junior-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Junior Data Engineer - **Company:** EDFT - **Location:** London, UK - **Experience:** Starter - **Salary:** £42,214.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Apache HTTP Server, Microsoft Azure, Cloud Computing, Computer Programming, Data as a Services, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Software Debugging, Distributed Computing Environment, Python (Programming Language), Scrum Methodology, Software Tools, Standard Sql, SQL Databases, Data Streaming, Workflow Management Systems, Parquet, Data Logging, Data Processing, Google Cloud, Data Storage Technologies, Apache Spark, Git, Microsoft Fabric, Real Time Data, Apache Kafka, Data Lakehouse, Software Version Control, Data Pipelines, Databricks - **Published:** July 26, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5815361523 ## About the Role * 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. 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., * 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 ## Description 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., * 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Parquet, Delta, Iceberg & Ducklake - An introduction for developers](https://www.wearedevelopers.com/videos/100075-parquet-delta-iceberg-ducklake-an-introduction-for-developers) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [OLAP for AI Applications and why you should care](https://www.wearedevelopers.com/videos/100212-olap-for-ai-applications-and-why-you-should-care) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Software Engineer Salary London](https://www.wearedevelopers.com/magazine/252-software-engineer-salary-london) - [Where to Find Entry-Level Software Engineering Jobs](https://www.wearedevelopers.com/magazine/397-where-to-find-entry-level-software-engineering-jobs) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)