ASOS

Retail

DigitalAnalyticsEngineer

£65–95k ~AI est. London, England, United Kingdom FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for mid candidates.

The Brief

“Digital Analytics Engineer at ASOS. Skills: Analytics engineering, Behavioural data modelling, Data pipelines, Data quality. Build behavioural models. Extend behavioural models”

What You'll Achieve.

Enable confident decisions; Run high quality experiments

Industry & Context.

Retail
Problems you'll solve

Identify issues; Resolve issues

What They're Looking For.

Must Have

Analytics engineering experience, Data engineering experience, Product analytics experience, SQL experience, Databricks experience, Spark experience, DBT experience, Python experience, Behavioural data modelling, Event based data modelling, Product analytics platforms experience, Reliable data pipelines, Quality controls experience, Work with software engineers, Data instrumentation experience

Nice to Have

Experimentation experience, A/B testing experience, Identity resolution knowledge, Cross device tracking knowledge, Power BI semantic modelling, Self serve analytics enablement, AI assisted analytics interest, Metric driven agents interest

What You'll Do.

Build behavioural models

Extend behavioural models

Maintain session logic

Design attribution logic

Maintain attribution logic

Design engagement metrics

Maintain engagement metrics

Design experiment datasets

Maintain experiment datasets

Own data pipeline quality

Own data pipeline consistency

Ensure event schema conformance

Ensure event naming conformance

Ensure data type conformance

Ensure required field conformance

Ensure privacy compliance

Build transformation pipelines

Maintain transformation pipelines

Act as technical owner

Implement data quality checks

Monitor schema changes

Monitor validation failures

Monitor event completeness

Monitor event coverage

Monitor cardinality drift

Monitor volume anomalies

Monitor identity integrity

Monitor user stitching integrity

Enable trusted metrics

Ensure metrics are usable

Partner with analysts

Partner with data teams

Partner with product teams

Ensure metrics are clear

Ensure metrics are consistent

Ensure metrics are reusable

Ensure instrumentation meets needs

Support event payload design

Support schema design

Support instrumentation PR reviews

Support pre-release validation

Support experiment tagging

Support exposure tracking

How You'll Work.

Team & Collaboration

Product teams; Engineering teams; Software engineers; Product analysts; Data teams; Product teams

Full Job Description

We’re looking for a Digital Analytics Engineer to help shape how ASOS understands customer behaviour across our digital estate. This role sits at the heart of digital analytics and experimentation, combining analytics engineering, behavioural data modelling, and close partnership with product and engineering teams. You’ll ensure behavioural data is well designed, observable, and trusted, enabling teams to make confident decisions and run high quality experiments at scale. 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 repo

Free ATS check

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