Associate Data Engineer
ASOS
In this role you will design and deliver scalable data solutions on the Azure platform to enable data-driven insights across ASOS. You will collaborate with cross-functional teams to improve engineering standards and practices, building reliable, well-tested data systems with automation. You’ll explore new tools, contribute to POCs, and stay curious about emerging data technologies to add business value. This is a chance to grow technically while helping ASOS scale its data capability in a fast-paced retail environment.
Pay / Benefits- ASOS employee discount
- Employee sample sales
- 25 days paid annual leave + extra celebration day
- Private medical care scheme
- Fixed annual payment in addition to salary
- Personalised learning opportunities
- Design and deliver data solutions on the Microsoft Azure Data Platform
- Collaborate with engineers through pairing, code reviews, and shared problem-solving
- Assist teams in understanding data, telemetry, and operational issues
- Experiment with new tools and approaches via proof-of-concept work, focusing on performance, data modeling, and cost
- Work with Lead Data Engineers, Engineering Managers, and Solution Architects to raise standards and practices
- Build solutions that are reliable, well-tested, and automated through modern CI/CD tooling
- Contribute to an agile, cross-functional team and share ownership of delivery quality
- Stay curious about emerging data technologies and apply them where they add business value
- Experience delivering data solutions end to end
- Good problem-solving and analytical skills
- Clear and thoughtful communication skills
- A collaborative, supportive working style
- Experience working with cloud-based data platforms
- An understanding of how to build reliable, well-tested data systems
- Collaborative
- Communicative
- Problem-solving
- Azure data technologies (e.g. Azure Cosmos DB, Azure SQL Database, Azure Data Factory)
- Python or another object-oriented programming language
- Azure Databricks and/or Apache Spark
Reference: WJ-747_30170975