Digital Analytics Engineer

Overview

In this role you will shape how ASOS understands customer behaviour across web and app by building reliable behavioural models and ensuring high-quality data for analytics and experimentation. You will partner with product and engineering teams to design instrumentation, maintain data quality, and enable trusted metrics for self-serve analysis and leadership reporting. This position offers impact through scalable data products and a strong focus on privacy-conscious, observable data.

You will work across Databricks, pipelines, and semantic layers to empower data-driven decisions.

• employee discount (hello ASOS discount!)
• employee sample sales
• 25 days paid annual leave + extra celebration day
• private medical care scheme
• Fixed Annual Payment in addition to salary
• learning opportunities and in-the-moment experiences

• Model and extend core behavioural models in Databricks for web and app
• Design and maintain session logic, funnels, journeys, attribution, and feature usage metrics
• Own data quality and consistency of behavioural events entering analytics platforms
• Build transformation pipelines for enrichment and standardisation; manage event contracts with frontend teams
• Implement end-to-end data quality checks and monitor schemas, completeness, and drift
• Enable trusted metrics via Databricks metric-enabled views and Power BI semantic models
• Collaborate with product analysts and engineers to ensure clear, reusable metrics
• Align frontend instrumentation with analytics needs and review instrumentation changes

• Experience in analytics engineering, data engineering, or product analytics
• Strong SQL and hands-on experience with Databricks / Spark / DBT / Python
• Solid understanding of behavioural and event-based data modelling
• Experience with product analytics platforms (Mixpanel, Adobe or similar)
• Experience building reliable data pipelines and quality controls
• Comfortable working with software engineers on data instrumentation
• Pragmatic, detail-oriented approach to data quality

• collaboration with cross-functional teams
• attention to detail
• pragmatism in tackling data quality issues
• Databricks
• Spark
• DBT