Digital Analytics Engineer

In this role you shape how ASOS understands customer behavior across web and app, building trusted, observable behavioural data for analytics and experimentation at scale. You will partner with product and engineering to design data models, pipelines, and semantic layers that enable self-serve analysis and leadership reporting. The role focuses on ensuring data quality, governance, and actionable metrics that drive smarter product decisions. It offers impact across the digital estate, experimentation, and cross-functional collaboration.

• employee discount
• employee sample sales
• 25 days paid annual leave + 1 extra day
• private medical care scheme
• fixed annual payment as a thank you
• personalised learning opportunities

• Develop and extend core behavioural models in Databricks for web and app interactions
• Design and maintain session logic, funnels, journeys, attribution, feature usage, engagement metrics, and experiment datasets
• Create domain-specific behavioural marts optimized for analytics and experimentation
• Own data pipeline quality and consistency into analytics platforms; enforce schemas, naming, data types, and privacy controls
• Build and maintain transformation pipelines for enrichment/standardisation; own event contracts between frontend and analytics teams
• Implement end-to-end data quality checks with software engineers; monitor schema changes, event completeness, cardinality drift, and volume
• Enable trusted metrics via Databricks metric-enabled views and Power BI semantic models for self-serve and leadership reporting
• Collaborate with frontend engineers on instrumentation design, validation, tagging, and exposure tracking
• Serve as the go-to expert for behavioural tracking best practices
• Partner with product analysts and data/product teams to ensure metrics are clear, consistent, and reusable

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

• collaboration with cross-functional teams
• attention to detail
• pragmatic problem solving
• SQL
• Databricks / Spark
• DBT