At a Glance
- Tasks: Build and evolve a cutting-edge data science platform used by millions.
- Company: Join ASOS, a global fashion retailer that values creativity and individuality.
- Benefits: Enjoy 25 days annual leave, private medical care, and personalised learning opportunities.
- Other info: Dynamic work environment with a focus on continuous improvement and career growth.
- Why this job: Make a real impact in tech while collaborating with talented engineers and data scientists.
- Qualifications: Experience in data platforms, Python, and Azure technologies is essential.
The predicted salary is between 63000 - 77000 Β£ per year.
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.
Everyone needs some help showing up as their best self. We're Disability Confident Committed - let our Talent team know if you need any reasonable adjustments throughout the recruitment process.
Responsibilities:
- Contribute to building and evolving the platform (infrastructure + reusable abstractions) that standardises data engineering workloads (batch/streaming pipelines, data processing) and traditional ML workflows (feature engineering, training, batch/real-time serving) across teams.
- Implement platform-level IaC, CI/CD, and environment management to support consistent, reproducible workloads across dev/test/prod.
- Build and maintain components using Python and Spark for data processing, shared datasets, and platform services.
- Contribute to shared services for data and ML lifecycle management (data pipelines, experiment tracking, versioning, lineage, permissions), aligned to enterprise governance (e.g. Unity Catalog).
- Support the implementation and operation of a centralised AgentOps capability (LLM gateway, tool integration, prompt and version management).
- Contribute to agent-specific lifecycle and safety controls (evaluation pipelines, guardrails, access control), with guidance from senior engineers.
- Enhance observability across both domains: Agent Ops: traces, responses, evaluations, cost and behaviour monitoring.
- Contribute to problem solving across platform reliability, performance, and security for data, ML, and agent workloads.
- Apply security and compliance best practices (RBAC/ACLs, secure configuration, identity and access management), supporting a secure-by-default platform design.
- Collaborate with Data Engineers, Data Scientists, and ML Engineers to enable adoption of platform capabilities across ASOS Tech.
- Contribute to documentation, standards, and best practices across the platform.
Experience:
- Experience in Data Platforms, Data Engineering, Cloud Engineering, or ML Platform Engineering roles, with exposure to Azure.
- Strong hands-on experience with Python and Apache Spark.
- Experience with Azure data platform technologies such as Azure Databricks, ADLS Gen2, and Unity Catalog.
- Working knowledge of security and access management (RBAC, ACLs, identity concepts such as Entra ID).
- Familiarity with Infrastructure as Code using Terraform.
- Experience with CI/CD (Azure DevOps, GitHub Actions).
- Exposure to Docker/Kubernetes in cloud environments is beneficial.
- Awareness of AgentOps patterns (LLM gateways, prompt/version control, evaluation, observability) is a plus.
- Good communication and collaboration skills, with a strong focus on learning and continuous improvement.
Employee benefits include:
- 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.
Platform Engineer β Data Science & AI Platform employer: asos.com Ltd
ASOS is an exceptional employer that fosters a vibrant and inclusive work culture, perfect for creative minds eager to make an impact in the fashion industry. With a focus on employee development, ASOS offers numerous growth opportunities and encourages innovation within its teams, ensuring that every voice is heard. Located in a dynamic environment, employees benefit from a collaborative atmosphere that thrives on cultural relevance and community engagement, making it a truly rewarding place to work.
StudySmarter Expert Adviceπ€«
We think this is how you could land Platform Engineer β Data Science & AI Platform
β¨Get Involved in Data Science Meetups
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We think you need these skills to ace Platform Engineer β Data Science & AI Platform
Some tips for your application π«‘
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. Donβt forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at asos.com Ltd, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why youβre a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at asos.com Ltd. 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.com Ltd
β¨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, itβll really make us stand out!
β¨Get Comfortable with Python and R
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β¨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how weβd 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.