Senior Data Engineer - Data Science Platform

Overview

As a Data Platform Engineer at ASOS, you help design and evolve the Azure-based data platform that powers ML and analytics for forecasting, recommendations, pricing, marketing and customer experiences. You will build scalable data pipelines, own platform components, and improve observability and reliability to enable data scientists and engineers to deliver data products at scale.

This role sits in the Data Science Platform group and partners with cross-functional teams to drive data-driven value for the business. You’ll shape engineering practices and platform capabilities that unlock efficient, reliable data solutions across ASOS.

Pay / Benefits

• hello ASOS discount
• employee sample sales
• 25 days paid annual leave + an extra celebration day
• Discretionary bonus scheme
• Private medical care scheme
• Flexible benefits allowance

Responsibilities

• Design and build data platform capabilities supporting data and ML workloads
• Develop high-performance data pipelines using Python, Scala, Spark and Databricks
• Own and evolve platform components for building, testing, deploying and monitoring data products
• Improve platform reliability, observability, data quality and operational excellence
• Create reusable libraries, tooling and engineering patterns to enable faster delivery of data products
• Partner with Data Scientists, ML Engineers and Product Engineering teams to enable new ML use cases
• Contribute to architectural decisions and data engineering standards
• Optimize distributed workloads for performance, scalability and cost in Azure ecosystem
• Support long-term development of ASOSs data platform and engineering practices
• Collaborate with multiple Data Science and ML teams to deliver platform capabilities that create business value

Key requirements

• Experience building or operating large-scale data platforms or data-intensive applications in a cloud environment
• Experience with Databricks, Spark and distributed data processing technologies
• Production-grade data engineering with Python and/or Scala
• Data architectures balancing scalability, reliability and cost efficiency
• Modern engineering practices including CI/CD, automated testing, observability and Infrastructure as Code
• Ability to solve complex engineering problems and improve platform capabilities
• Experience leading design and delivery of complex data engineering solutions and contributing to technical direction
• Mentoring engineers through technical guidance, code reviews and knowledge sharing
• Ability to collaborate across teams and stakeholders to balance business priorities with technical excellence
• Collaboration across teams
• Mentoring and knowledge sharing
• Balancing business priorities with technical excellence
• Databricks
• Spark
• Python

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