Senior Data Engineer
AI in this role
We’re ASOS, the online retailer for fashion lovers all around the world.
We exist to give our customers the confidence to be whoever they want to be, and that goes for our people too. At ASOS, you’re free to be your true self without judgement, and channel your creativity into a platform used by millions.
But how are we showing up? We’re proud members of Inclusive Companies, are Disability Confident Committed and have signed the Business in the Community Race at Work Charter, and we placed 8th in the Inclusive Top 50 Companies Employer list.
Everyone needs some help showing up as their best self. Let our Talent team know if you need any adjustments throughout the process in whatever way works best for you.
Join the team responsible for building the data and machine learning products that power customer experiences, marketing effectiveness and commercial decision-making across ASOS.
As a Senior Data Engineer in our Customer & Marketing Machine Learning team, you will play a key role in developing the data foundations that enable machine learning models, pricing optimisation, customer analytics, media measurement and marketing decisioning at scale. The team brings together Data Engineers, Machine Learning Engineers and Applied Scientists to deliver end-to-end machine learning products that create measurable business impact.
This is an opportunity to work with large-scale customer, marketing and commercial datasets, building robust data solutions that help transform how ASOS understands, engages and serves millions of customers.
What you'll be doing
- Designing and building scalable data pipelines that support machine learning, analytics and data science initiatives.
- Developing high-performance data solutions using Python, Spark, Databricks and Azure technologies.
- Creating and maintaining datasets that power customer, marketing, pricing and forecasting use cases.
- Collaborating closely with Applied Scientists and Machine Learning Engineers to operationalise machine learning models.
- Designing data models and architectures that enable reliable, trusted and accessible data across the business.
- Improving data quality, observability, lineage and monitoring capabilities across critical data products.
- Supporting the delivery of media measurement, customer value, pricing and personalisation initiatives.
- Optimising data processing workloads for performance, scalability and cost efficiency.
- Driving best practices across software engineering, data engineering and platform development.
- Providing technical leadership through design reviews, mentoring and knowledge sharing.
- Working with stakeholders across Customer, Marketing and Commercial functions to turn business challenges into scalable data solutions.
- Contributing to the long-term evolution of ASOS's machine learning and data capabilities.
We're interested in people who can demonstrate many of the following capabilities. If your experience doesn't match every requirement exactly, we still encourage you to apply.
You are likely to have:
- Significant experience delivering data engineering solutions within cloud-based environments.
- Strong expertise in Python and experience developing production-grade data pipelines and data products.
- Experience working with Databricks, Spark and distributed processing technologies.
- Experience designing and operating large-scale data platforms using Azure, AWS or GCP.
- Deep understanding of data modelling, data architecture and modern data engineering principles.
- Experience enabling data science, analytics or machine learning workloads through robust data infrastructure.
- Strong understanding of CI/CD, Infrastructure as Code, automated testing and observability practices.
- Experience working with large-scale customer, marketing, commercial or behavioural datasets.
- Proven ability to lead the design and delivery of complex technical solutions from concept through to production.
- Experience mentoring engineers and helping shape technical direction within a team.
- Strong stakeholder management and communication skills, with the ability to partner effectively with technical and non-technical audiences.
- A passion for solving challenging data problems and enabling teams to make better decisions through data.Experience supporting machine learning platforms, MLOps or model operationalisation.
- Experience with pricing, marketing analytics, recommendation systems or customer analytics.
- Experience working in eCommerce, retail or consumer-facing digital businesses.
BeneFITS’
- Employee discount (hello ASOS discount!)
- Employee sample sales
- 25 days paid annual leave + an extra celebration day for a special moment
- Discretionary bonus scheme
- Private medical care scheme
- Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits
- Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role
How we rate this
Senior Data Engineer at ASOS rates 11 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Walk me through how you've used Databricks in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: ML Ops and Databricks. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
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