Senior Data Engineer - Data Science Platform at ASOS

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

12 Aug, 26

Salary

0.0

Posted On

14 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Scala, Spark, PySpark, Azure Data Factory, Azure Databricks, Delta Lake, dbt, Terraform, CI/CD, Data Modelling, Data Pipeline Design

Industry

Retail Apparel and Fashion

Description

Company Description 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. Job Description As a Senior Data Engineer, you’ll focus on designing and implementing scalable, reusable data pipelines, platform components, and data engineering standards that enable reliable, secure, and high-quality data solutions across the organisation. You’ll work closely with Data Scientists, Analysts, and Engineers embedded in product teams such as Forecasting, Recommendations, Marketing, Customer, and Pricing—helping them accelerate delivery and improve the quality and accessibility of data by providing a robust and standardised data platform experience. What you’ll be doing Designing, building, and maintaining scalable data pipelines using Python and Scala, leveraging Spark and PySpark within an Azure ecosystem (including Azure Data Factory and Databricks). Developing and maintaining reusable data engineering templates, frameworks, and tooling to support data teams across ASOS. Driving standardisation and best practices across data ingestion, transformation, and serving layers to ensure consistency across diverse product domains. Enabling teams to deliver high-quality, production-ready datasets by providing guidance, patterns, and hands-on technical support. Implementing and promoting modern data engineering practices — including CI/CD for data pipelines, data quality validation, testing, observability, and metadata management. Collaborating with stakeholders to understand data requirements and evolving the data platform to meet business needs. Partnering with Platform Engineering, ML Engineering, and Security teams to ensure scalable, cost-efficient, and secure data infrastructure on Azure. Optimising data workflows and pipelines for performance, reliability, and cost efficiency. We believe being together in person helps us move faster, connect more deeply, and achieve more as a team. That’s why our approach to working together includes spending at least 2 days a week in the office. It’s a rhythm that speeds up decision-making, helps ASOSers learn from each other more quickly, and builds the kind of culture where people can grow, create, and succeed. Qualifications Strong experience as a Data Engineer building scalable data platforms Deep expertise in Azure (ADF, ADLS, Databricks) Proficiency in Python and/or Scala (PySpark/Spark) for large-scale data processing Hands-on experience with Databricks and Delta Lake Solid understanding of the end-to-end data lifecycle (ingestion → transformation → serving) Experience with dbt for transformations and Terraform for infrastructure as code Familiarity with CI/CD pipelines and modern data engineering best practices Strong grounding in data modelling, quality, and testing Experience with monitoring, observability, and performance optimisation Focus on automation, standardisation, and improving developer experience Additional Information BeneFITS’ Employee discount (hello ASOS discount!) Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits Private medical care scheme Discretionary bonus scheme 25 days paid annual leave + an extra celebration day for a special moment Employee sample sales

Responsibilities

Design and implement scalable, reusable data pipelines and platform components within an Azure ecosystem to support various product teams. Drive standardization and best practices for data ingestion, transformation, and serving layers across the organization.