At a Glance
- Tasks: Shape customer behaviour insights through data modelling and analytics engineering.
- Company: Join ASOS, a leading fashion retailer with a focus on innovation.
- Benefits: Enjoy employee discounts, private medical care, and 25 days annual leave.
- Other info: Dynamic team environment with opportunities for personal growth and learning.
- Why this job: Make a real impact on how ASOS understands its customers and drives decisions.
- Qualifications: Experience in analytics engineering and strong SQL skills required.
The predicted salary is between 50000 - 65000 £ per year.
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 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
Qualifications 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
Additional Information
- 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
Digital Analytics Engineer in London employer: ASOS
ASOS is an exceptional employer that prioritises employee growth and well-being, offering a vibrant work culture where innovation thrives. As a Digital Analytics Engineer, you'll enjoy a competitive salary, generous benefits including a substantial employee discount, private medical care, and 25 days of annual leave, all while working in a collaborative environment that encourages personal development and meaningful contributions to customer understanding.
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We think this is how you could land Digital Analytics Engineer in London
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We think you need these skills to ace Digital Analytics Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at ASOS. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at ASOS
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at ASOS!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
