ASOS Technology is going through an exciting period of transition and major investment. – this includes a number of strategic programmes to deliver the amazing technology and business solutions to support our ambitious global growth plans. At the heart of these plans is the rebuilding of our digital platforms and channels to provide the best shopping experience for our customers. Our plan is designed to enable us to really put our mobile experience first, enable personalisation and support a data driven organisation. We are also making significant investments in all our Buying, Merchandising, Finance and People systems with the latest toolsets and applications to accelerate the next phase of our global growth. We are also improving our ways of working within Technology to enable autonomous platform development and improve our engineering and agile practices.

ASOS is the UK’s number one fashion and beauty destination, expanding globally at a rapid pace. Our values are to be authentic, brave and creative, and we live and breathe these in everything we do. Our award-winning Tech teams sit at the heart of our business.  

We deliver technical innovations and pioneer incredible solutions to keep our 20-something market engaged, the cloud-based architecture to support our global reach and the agile engineering methods to deliver value fast. We’re extremely ambitious and thrive on the individuality of our amazing employees. Our values encompass everything needed for our tech people to be the thought leaders of tomorrow, and our roles offer a flexible blend of remote and in-office working. 

  

Big Data engineers in the AI & Data Science (DS) platform are software/data engineers who specialise in big data, and its associated preparation, processing and provision for use by Data and Machine Learning Scientists.   

What you'll be doing 

• Ensuring that data pipelines are scalable, repeatable, and secure, and can reliably provide the quality and throughput of data required for complex machine learning workloads using extremely large datasets 

• Working as part of an enthusiastic and motivated development team comprised of software engineers, scientists, platform engineers and analysts, to ensure the technical requirements of our numerous stakeholders are met in the most efficient way.  

• Delivering high-quality software which is future proof, scalable and in-keeping with ASOS standards.  

• Using the latest tech, such as Azure Data Factory, Azure Databricks, Spark, Event Hub + Azure Stream Analytics. 

• Research and experiment with emerging data technologies and industry trends with a view to bringing business value through early adoption 

• Joining our regular Tech Develops days to learn new things, take part in internal and external hackathons, share your knowledge and help to drive improvements in engineering.  

• Continually develop and improve our code and technology, whilst playing an active role in the conception of brand-new features for our millions of global customers.  

  

Key Skills and Experience 

• Strong understanding of Big Data core concepts and technologies 

• Cloud experience (Azure/AWS/Google) 

• Comprehensive programming knowledge, with an understanding of OOP principles and cloud engineering practices 

• Skills in Java, Scala, Python, R (one or more) 

• Azure Data Factory 

• Azure Databricks 

• Experience in building real time/batch data, high throughput processing infrastructure 

• Knowledge of RDBMS, ETL and Data Warehouse Technologies 

• Fully-automated provisioning and application deployment pipelines 

• Exposure to CI/CD tools like Azure DevOps, Ansible, TeamCity, Jenkins, Octopus 

• Infrastructure automation using a scripting language like bash or powershell 

• Configuration management 

• Experience in delivering services that are highly-available, low-latency and scalable 

Nice to Have: 

• Deep exposure to Microsoft Azure  

• Exposure to a microservices architecture 

• Cassandra, MongoDB or equivalent NoSQL databases 

• Knowledge of advanced analytics and insights techniques (e.g. predictive analytics, machine learning, segmentation) 

• Knowledge of deep learning frameworks like TensorFlow, Keras 

• Knowledge of machine learning libraries like MLLIB, sklearn 

• Experience in retail and/or e-commerce