Data Engineer

(EDO) Entertainment Data Oracle, Inc.
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer - AI Analytics and EdTech Developments

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

We are a new generation consultancy based across UK and EU and founded on the premises of the engineering excellence and empowering people to make an impact. All our consultants have equity in the company, genuinely love what they do and are really good at it.

We work with all modern tech stacks and typically run agile scrum on all our projects.

About you

Are you passionate about data and its transformational powers? Do you like being able to make a huge difference in a limited period of time? We might be just the right place for you.

Your key skills and capabilities:

  • Implementing cloud-native data platforms
  • Engineering scalable and reliable pipelines
  • Good knowledge of distributed computing with Spark
  • Understanding of cloud architecture principles and best practices
  • Hands-on experience in designing, deploying, and managing cloud resources
  • Excellent Python and SQL skills
  • Agile ways of working
  • Experience in cloud automation and orchestration using tools such as CloudFormation or Terraform
  • Monitoring and performance tuning of cloud-based applications and services

Nice to haves: (MLOps):

  • Model Deployment & Serving – Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance.
  • Monitoring & Retraining – Set up model drift detection, performance monitoring, and automated retraining.
  • ML Pipelines & CI/CD – Automate end-to-end ML workflows.

We expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform.

Our promise to you

We will always see you as a human being and will do our very best to support your needs and wellbeing – well-designed co-working and collaboration spaces, remote working patterns that work for you, parenting leave, sabbaticals and ability to work on personal projects.

We believe that a gelled team is worth its weight in gold – we will do everything we can to avoid breaking well-performing teams – your team will be stable across different projects and you will work with people you trust and like.

We are committed to prioritising the wellbeing of our employees. To fulfill this promise, we provide a comprehensive employee wellbeing program that includes mental health support, flexible working arrangements, wellness activities, and a positive work culture.

We recognise that the world of tech delivery has moved on significantly in the last 15 years and know a thing or two about how to bring projects over the line without experiencing lots of despair and burn-out. In fact, we like to believe that our projects are the opposite of that – they are run smoothly and most of the time are fun to work on.

Apply for this job

First Name *

Last Name *

Email *

Phone

Resume/CV

Accepted file types: pdf, doc, docx, txt, rtf


#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.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.