Digital Analytics Engineer

What You’ll Be Doing

Behavioural Data Modelling

• Build and extend core behavioural models in Databricks that describe how customers interact with ASOS across web and app
• Design and maintain:
• Session logic
• Funnels and journeys
• Attribution logic
• Feature usage and engagement metrics
• Experiment exposure and variant datasets
• Create domain specific behavioural marts optimised for analytics and experimentation use cases

Web Analytics Data Pipeline Ownership

• Own the quality and consistency of behavioural events flowing into Analytics platforms
• Ensure events conform to agreed:
• Schemas and naming conventions
• Data types and required fields
• Privacy first compliance
• Build and maintain transformation pipelines where enrichment or standardisation is required
• Act as a technical owner of event contracts between frontend teams and analytics

Data Quality & Observability

• In collaboration with the teams software engineers implement end-to-end data quality checks across frontend → ingestion → Analytics → Databricks
• Monitor and alert on:
• Schema changes and validation failures
• Event completeness and coverage
• Cardinality drift
• Volume anomalies
• Identity and user stitching integrity
• Proactively identify and resolve issues before they impact experiments or reporting

Semantic Layer Enablement

• Enable trusted behavioural metrics through:
• Databricks metric enabled views
• Power BI semantic models
• Ensure metrics are usable for:
• Self serve analysis
• Executive and leadership reporting
• “Talk to Data” and agent based workflows
• Partner with product analysts, data and product teams to ensure metrics are clear, consistent, and reusable

Frontend Instrumentation Alignment

• Work closely with web and app engineers to ensure instrumentation meets analytics and experimentation needs
• Support:
• Event payload and schema design
• Instrumentation PR reviews
• Pre‑release validation
• Experiment tagging and exposure tracking
• Act as a go to expert for behavioural tracking best practices

We’re Looking For

Core Skills & Experience

• Experience in analytics engineering, data engineering, or product analytics
• Strong SQL and experience working in Databricks / Spark / DBT/ Python
• Solid understanding of behavioural and event based data modelling
• Hands‑on experience with product analytics platforms (e.g. Mixpanel, Adobe or similar)
• Experience building reliable data pipelines and quality controls
• Comfortable working closely with software engineers within product teams on data instrumentation
• A pragmatic, detail oriented approach to data quality

Nice to Have

• Experience supporting experimentation and A/B testing
• Knowledge of identity resolution and cross device tracking
• Power BI semantic modelling experience
• Experience enabling self serve analytics
• Interest in AI assisted analytics or metric driven agents
• Employee discount (hello ASOS discount!)
• Employee sample sales
• 25 days paid annual leave + an extra celebration day for a special moment
• Private medical care scheme
• Fixed Annual Payment in addition to your salary each year, it's just an extra thank you from us
• Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role