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