Senior Data Engineer

Deliveroo
City of London
4 days ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Energy

Senior Data Engineer, SQL, RDBMS, AWS, Python, Mainly Remote

Join to apply for the Senior Data Engineer role at Deliveroo


Why Deliveroo

Deliveroo’s mission is to transform the way you shop and eat, bringing the neighbourhood to your door by connecting consumers, restaurants, shops and riders. We obsess about building the future of food, whilst using our network as a force for good. We're at the forefront of an industry, powered by our market‑leading technology and unrivalled network to bring incredible convenience and selection to our customers.


The Role

As part of the Analytics Engineering team, you’ll help design, build, and scale the core data and analytics platforms that power decision‑making across Deliveroo. You’ll work on the systems and tooling that form the foundation of our data ecosystem — ensuring reliability, performance, and a seamless experience for the analytics engineers, data scientists, and business teams who depend on them.


As a Data Engineer, your focus will be on evolving our core data platform capabilities — from data modelling frameworks and batch/real‑time data pipelines to governance solutions. You’ll play a key role in automating, optimising, and extending these systems to make data more discoverable, trustworthy, and actionable across the organisation.


This is a hands‑on engineering role where you’ll combine deep technical expertise with a product mindset, developing solutions that empower others to deliver insights and drive growth. You’ll also contribute across the wider data stack, from building infrastructure and automation pipelines to contributing to design discussions and engineering best practices.


This is an exciting period for the team as we begin to integrate our data platforms with our new global counterparts at DoorDash and Wolt, opening up new opportunities to shape a world‑class, unified analytics ecosystem.


Key Responsibilities

  • Design, build, and maintain robust data platform components, including pipelines, orchestration, and modelling frameworks
  • Develop automation and tooling that improve efficiency, scalability, and data quality across the analytics stack
  • Enhance and support platform reliability and observability, participating in on‑call rotations and proactive issue resolution
  • Support and optimise governance and metadata systems to improve discoverability, trust, and compliance across data assets
  • Partner with analysts, analytics engineers, and data scientists to understand user needs and deliver impactful platform solutions
  • Contribute to the wider engineering community through design reviews, code reviews, documentation, and knowledge sharing, collaborating globally with teams across Deliveroo, DoorDash, and Wolt to define shared data standards and evolve our platform architecture

Skillset

We want to emphasise that we don’t expect you to meet all of the below but we would love for you to have experience in some of the following areas:



  • Proficiency in modern data engineering practices and technologies, including Prefect/Airflow, Python, dbt, Kubernetes, Kafka or similar
  • Experience with Infrastructure as Code (IaC) and cloud‑based services e.g. deploying infrastructure on AWS using Terraform
  • A deep understanding of data pipelines, orchestration, and data modelling practices
  • A product‑oriented mindset, focused on enabling analysts, analytics engineers, and end users to deliver scalable, high‐impact data products
  • A proven ability of building scalable, maintainable, and automated data systems that add measurable business value
  • A collaborative, cross‑functional approach to problem‑solving and system design
  • Curiosity and initiative to explore new technologies and ways of working, especially in a global and evolving environment
  • Expertise in modern, agile software development processes

Workplace & Benefits

At Deliveroo we know that people are the heart of the business and we prioritise their welfare. Benefits differ by country, but we offer many benefits in areas including healthcare, well‑being, parental leave, pensions, and generous annual leave allowances, including time off to support a charitable cause of your choice. Benefits are country‑specific, please ask your recruiter for more information.


Diversity

We believe a great workplace is one that represents the world we live in and how beautifully diverse it can be. That means we have no judgement when it comes to any one of the things that make you who you are – your gender, race, sexuality, religion or a secret aversion to coriander. All you need is a passion for (most) food and a desire to be part of one of the fastest growing start‑ups around.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.