Senior Data Engineer

ASOS Technology is going through an exciting period of transition towards product operating model - this includes several strategic programmes to deliver the amazing technology and business solutions to support our ambitious global growth plans. ASOS is a unique and forward-thinking company which understands the data opportunities and technology has on driving the business forward. The Digital Data Engineering team is responsible for providing high quality, scalable data solutions across the ASOS Digital Domain, advocating best practices, modern technologies and educating development teams. As a Data Engineer, you'll be hands-on delivering business critical projects and processes, helping the digital platforms to fully take advantage of their data while ensuring continuous product improvement, best practices usage and ongoing development of engineers through pairing. From problem solving to new concept ideas, you have the genuine opportunity to unleash your full potential and creativity on a variety of ASOS Data engineering projects

What youll be doing:

1. Developing solutions using Microsoft Azure and Databricks to the highest standard in a way that favours a simple and maintainable approach over a complex one, is tested at every step on the path to production, using the appropriate tools and practices, is deployable using automated tooling only, and is suitably instrumented to meet core operational and business monitoring needs.

2. Optimising Data pipelines, frameworks, and systems to facilitate easier development of data artifacts.

3. Supporting production applications such as Data Pipelines, Core data infrastructure etc.

4. Developing proof of concepts by exploring innovative ways to utilise our data and presenting findings to Product teams. This also involves conducting feasibility testssuch as performance evaluations, data modelling, cost analysis and etc

5. Working with engineering team leads and Product managers to set and maintain standards and development practices that promote high delivery cadence without impeding robust, scalable, and quality solutions.

6. Working with an agile, cross functional team, taking responsibility for the engineering team deliverables and quality.

7. Keep up to date with emerging data technologies and industry trends with a view to bringing business value through early adoption.

8. Building strong and productive relationships across business, science, analytics, and engineering partners

Previous experience in a Data Engineering role:

1. We're looking to meet experienced Data Engineers who live and breathe Data, who seek to take this best practice and use it in a dynamic environment while making waves in the industry.

2. In-depth knowledge in most of the following technologies: o Proficient in Spark/PySpark with hands-on experience. o Azure data related technologies such as Azure Databricks, Azure Data Factory, Azure Event Hubs and Azure Service Bus. o Solid programming skills in Python or Scala, and strong command of SQL. o Experience with testing frameworks like pytest, ScalaTest, or similar. o Open table formats such as Delta, Iceberg or Apache Hudi. o Experience in leveraging CI/CD workflows to automate the building, testing, publishing, releasing, and deployment of code using tools like Azure DevOps, GitHub Actions, and distributed version control systems such as GIT. o Understanding of cloud infrastructure and Infrastructure as code (Terraform or Bicep)

3. Excellent communication skills.

4. Good analytical and problem-solving skills.

5. Broad expertise in the delivery of data product solutions.

6. Commitment to provide a high level of customer service to internal and external users.

7. Proven experience in the successful delivery and ongoing maintenance of complex, high volume, high performing, and high-quality software systems.

8. Experience of implementing/supporting cloud-based solutions.

9. Familiarity with agile methodologies, such as Scrum or Kanban.

Nice to Have:

1. Experience in retail and/or e-commerce.

2. Understanding of Big Data and Distributed Computing.

3. Understanding of streaming technologies (Spark Structured streaming, Apache Flink, etc)

4. Other programming languages: PowerShell, Bash.

5. Understanding of Databricks Ecosystem (Unity Catalog, Workflows, Delta Live Tables).

6. Understanding of any Data Observability or Data Quality Framework