More about the role:
As a Junior Data Engineer, you are a core member of our technical team. You will support the development and maintenance of our AI models and data pipelines. This is a hands-on coding role where you will learn to apply your technical skills to real-world data problems, working with large datasets and learning from our senior data scientists. In addition, you will be responsible for the delivery of AI-based features into our products and contributing to DevOps for Burson owned platforms.
The role is hybrid, based in London. You will be part of a distributed intelligence team embedded into multiple product teams, collaborating with colleagues from diverse backgrounds, geographies, and functions.
What you'll do:
1. Write Python scripts to clean, transform, and prepare large datasets for analysis and modelling.
2. Assist in the training and validation of machine learning models.
3. Run pre-written code and queries to support ongoing analysis and product operations.
4. Support the maintenance of our data infrastructure within the Azure cloud environment.
5. Conduct exploratory data analysis to help identify patterns and opportunities.
Experience that contributes to success:
6. Qualification in Computer Science or comparable practical experience
7. Experience in designing and implementing end to end business intelligence solutions
8. Familiarity with agile working methodologies, such as scrum and kanban, agile development methodologies and the DevOps culture of collaboration, communication, and continuous improvement.
9. Experience with version control systems such as Git, and knowledge of best practices for collaboration and code review
10. Understanding of RESTful API design principles and experience with API development frameworks such as Flask
11. Ability to use tools like Python Unit Tests to identify and fix bugs, as well as writing automated tests to ensure code quality and reliability
12. Experience with connecting Power BI / Apps / Automate and SharePoint resources to data processing pipelines and storage in Azure stack
13. Knowledge of Microsoft Azure and experience with infrastructure and services including Data Factory, Azure DevOps, Fuction and Logic Apps
14. Proficiency in Python. Additional scripting languages and/or one or more programming languages such as Java, R, DAX, M is an advantage
15. SQL and NoSQL Databases and Data Lakes: Familiarity with SQL and NoSQL databases such as MongoDB, and in file formats such as Parquet for data storage and retrieval.
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