Junior Data Engineer

Curveanalytics
London
1 month ago
Applications closed

Related Jobs

View all jobs

Junior Data Engineer

Junior Data Engineer

Junior Data Engineer

Junior Data Engineer

Junior Data Engineer

Junior Data Engineer

Curve is a next-gen insights, analytics and technology consultancy that leverages digital consumer data and advanced technology to help businesses unlock consumer opportunities. Digital consumer data is powerful; it’s big, it’s real, and it’s always updating. We use a combination of in-house technology and bespoke solutions, powered by AI, to transform data from sources such as Social, Reviews, Search, and broader marketing and sales data. These reveal fresh insights for our clients; helping them to build better products and brands, to deliver effective marketing to consumers.

Our software, machine learning and AI are key to how we deliver impact, centred on:

  • Natural Language Processing, GPT & other LLMs: unearthing trends, themes and other patterns from large text-based data sets, and deploying state-of-the-art AI to automate and empower consumer facing businesses and their insights & analytics functions
  • Marketing Data Science & Personalisation: using first party consumer data to understand each client’s consumer base, building personalisation and other machine learning models to better engage with and excite consumers
  • Data Engineering & Data Architecture: data engineering across a variety of tools to integrate these leading technologies into optimised and efficient data models and ecosystems, feeding into best-in-class analytics dashboards, marketing activation and front-end platforms
  • Software Engineering: full stack expertise to build, maintain and support internal and externally facing Software & Data as a Service solutions, in AWS, that accelerate delivery and unlock deeper insights for our clients

As a start-up, we can move faster than most companies and do things differently. We have experienced rapid growth so far and we’re looking for a Junior Data Engineer to join our growing team.

ABOUT THE ROLE

You will play a crucial role in designing, building and productionising innovative data pipelines, in the cloud, from scratch. You’ll work on a mix of small analytics proof of concepts and larger projects, both of which push the boundaries of what we can do with data; finding and using novel data sources and APIs, and enriching them with leading analytics, data science and AI methods.

Your role will be twofold. You’ll be working directly with our London-based client-base, as well as helping to shape the future of our fast-growing start-up. We’ll let you challenge yourself, from your core of data engineering to support our data science and dashboard visualisation work, to grow your cloud architecture and engineering knowledge, and to understand the business and strategic impact of your great engineering work – to whatever extent suits you.

WHAT YOU’LL BE DOING

  • Build innovative data solutions
  • Support the development and rollout of an industry-first global analytics programme
  • Develop and deploy automated code pipelines, from data acquisition through cleaning and preparing data for modelling, through to visualisation
  • Help to productionise machine learning models
  • Work closely with a great programme team – project lead, data scientists and analysts – and interface with client technology counterparts
  • Identify ways to improve data reliability, processing efficiency and quality of our data output
  • Deploy pipelines in cloud environments and develop as a cloud technologist, as our world becomes increasingly reliant on cloud technologies
  • Produce detailed documentation and champion code quality
  • Interrogate rich data sources such as social, search, surveys, reviews, clickstream, sales, connected devices and beyond
  • Identify and explore opportunities to acquire new data sources that deliver innovative perspectives to our clients

WHAT WE’RE LOOKING FOR

  • Bachelor’s degree or higher in an applicable field such as Computer Science, Statistics, Maths or similar Science or Engineering discipline
  • Strong Python and other programming skills (Java and/or Scala desirable)
  • Strong SQL background
  • Some exposure to big data technologies (Hadoop, spark, presto, etc.)

NICE TO HAVES OR EXCITED TO LEARN:

  • Some experience designing, building and maintaining SQL databases (and/or NoSQL)
  • Some experience with designing efficient physical data models/schemas and developing ETL/ELT scripts
  • Some experience developing data solutions in cloud environments such as Azure, AWS or GCP – Azure Databricks experience a bonus
  • 30 minute video interview with the People & Operations Team
  • 45 minute technical video interview with one of our Senior Data Engineers
  • Final interview with our Partner, Head of Technology

Get to know Curve's journey and meet some of the minds fuelling our passion


#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.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

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.