Data Engineer (Food Delivery)

Coherent Solutions, Inc.
City of London
4 days ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

SolutionsAt Coherent Solutions, we offer engineering solutions that protect your time, your bottom line, and most importantly, your business.##### Our ApproachWe work collaboratively and transparently, providing regular updates and seeking feedback, to ensure that your needs are met, and the project is delivered successfully.# Data Engineer (Food Delivery)## Locations## Company BackgroundOur client is a leading online and mobile food ordering company operating in over 1,600 U.S. cities and London. With a portfolio of well-known food delivery brands, they serve millions of diners and process hundreds of thousands of orders daily. The company supports a large and growing restaurant network and prioritizes excellent customer experience through 24/7 support, innovative technology, and scalable infrastructure.## Project DescriptionThis role is part of the data engineering team focused on marketing analytics and reporting infrastructure. You’ll enhance and extend a big data platform that supports marketing activities, senior-level metrics dashboards, and customer engagement systems. The role offers a unique opportunity to design and build end-to-end solutions for data storage, transformation, and visualization - ensuring that business decisions are data-driven and scalable.## Technologies* Python* PySpark* SQL* Spark* AWS (S3, EMR)* Git* Jenkins* CI/CD* PyCharm* Distributed Systems## What You'll Do* Design, build, and maintain large-scale data pipelines using Spark and PySpark.* Develop robust ETL processes to support automated analytics, performance monitoring, and campaign reporting.* Collaborate with marketing and analytics stakeholders to translate business needs into technical requirements.* Write unit tests and leverage CI/CD tools to ensure high code quality and performance.* Support and improve existing data infrastructure while planning for long-term scalability.* Ensure efficient data processing using distributed systems and cloud-native services.* Contribute to architectural decisions and recommend best practices for data handling, modeling, and storage.## Job Requirements* 2+ years of experience in Spark and SQL development.* Solid Python programming experience with emphasis on data engineering.* Experience with PySpark in distributed environments.* Familiarity with cloud services (especially AWS S3 and EMR).* Knowledge of ETL, data modeling, and performance optimization.* Understanding of software engineering best practices (e.g., unit testing, CI/CD, Git).* Strong communication skills and ability to explain technical concepts clearly.* Attention to detail and a strong sense of ownership.* English - B2 or higher.## What Do We OfferThe global benefits package includes:* Technical and non-technical training for professional and personal growth;* Internal conferences and meetups to learn from industry experts;* Support and mentorship from an experienced employee to help you professional grow and development;* Internal startup incubator;* Health insurance;* English courses;* Sports activities to promote a healthy lifestyle;* Flexible work options, including remote and hybrid opportunities;* Referral program for bringing in new talent;* Work anniversary program and additional vacation days.We're always starting new projects and we'd love to work with you. Please send your CV and we'll get in touch.

#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.

Careers Jobs

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.

Jobs

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.

Education Jobs

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.