Senior Data Engineer - Data Science Platform in London

Senior Data Engineer - Data Science Platform in London

London Full-Time 60000 - 80000 £ / year (est.) Home office (partial)

At a Glance

  • Tasks: Design and implement scalable data pipelines using Python and Scala in an Azure ecosystem.
  • Company: Join a leading tech company focused on innovative data solutions.
  • Benefits: Enjoy employee discounts, flexible benefits, private medical care, and 25 days annual leave.
  • Other info: Collaborative office culture with opportunities for personal growth and development.
  • Why this job: Make a real impact by enhancing data accessibility and quality across diverse product teams.
  • Qualifications: Strong experience in data engineering with expertise in Azure and proficiency in Python or Scala.

The predicted salary is between 60000 - 80000 £ per year.

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:

  • 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.

Senior Data Engineer - Data Science Platform in London employer: 慨正橡扯

ASOS is an exceptional employer that fosters a collaborative and innovative work culture, particularly for the Senior Data Engineer role. With a strong emphasis on personal growth, employees benefit from tailored learning opportunities, flexible benefits, and a supportive environment that encourages teamwork and creativity. Located in a vibrant setting, ASOS offers unique advantages such as employee discounts and a commitment to work-life balance, making it an attractive place for those seeking meaningful and rewarding employment.

Contact Details:

慨正橡扯 Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior Data Engineer - Data Science Platform in London

Network Like a Pro

Get out there and connect with folks in the industry! Attend meetups, webinars, or even just grab a coffee with someone who’s already in the data engineering game. Building relationships can open doors that job applications alone can't.

Show Off Your Skills

Don’t just tell them what you can do—show them! Create a portfolio of your projects, especially those involving Python, Scala, and Azure. Share your GitHub link when you apply through our website; it’ll give you an edge over the competition.

Ace the Interview

Prepare for technical interviews by brushing up on your data pipeline knowledge and Azure skills. Practice common interview questions and be ready to discuss your past projects in detail. Confidence is key, so know your stuff!

Follow Up

After your interview, don’t forget to send a thank-you email! It shows your enthusiasm for the role and keeps you fresh in their minds. Plus, it’s a great opportunity to reiterate why you’re the perfect fit for the Senior Data Engineer position.

We think you need these skills to ace Senior Data Engineer - Data Science Platform in London

Data Engineering
Python
Scala
Spark
PySpark
Azure Data Factory
Databricks

Some tips for your application 🫡

Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Senior Data Engineer role. Highlight your expertise in Azure, Python, and Scala, and don’t forget to mention any experience with data pipelines and CI/CD practices.

Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about data engineering and how your background aligns with our mission at StudySmarter. Be sure to mention specific projects or achievements that showcase your skills.

Showcase Your Projects:If you’ve worked on relevant projects, whether in a professional setting or as personal endeavours, include them in your application. This gives us a glimpse into your hands-on experience and problem-solving abilities in real-world scenarios.

Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing applications come in through our own platform!

How to prepare for a job interview at 慨正橡扯

Know Your Tech Stack

Make sure you’re well-versed in the technologies mentioned in the job description, especially Azure, Python, and Scala. Brush up on your experience with Databricks and Delta Lake, as these will likely come up during technical discussions.

Showcase Your Problem-Solving Skills

Prepare to discuss specific examples where you've designed and implemented scalable data pipelines. Think about challenges you faced and how you overcame them, particularly in relation to data quality and performance optimisation.

Understand the Business Context

Familiarise yourself with ASOS’s business model and how data engineering supports various product teams. Be ready to discuss how you can help improve data accessibility and quality for teams like Forecasting and Marketing.

Emphasise Collaboration

Since the role involves working closely with Data Scientists and Engineers, be prepared to talk about your experience collaborating across teams. Highlight any instances where you’ve provided guidance or support to enhance team performance and standardisation.