Data Engineer

UST
City of London
2 months 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

This is a proactive pipelining initiative. We are not hiring for this role at the moment; however, we are building a pipeline of strong, qualified candidates. Once the position officially opens, we will reach out to shortlisted professionals to begin the interview process.


Location: London

Work mode: hybrid


About the Role:

We are seeking an experienced Data Engineer with deep expertise in Power BI and enterprise-scale reporting environments. The ideal candidate will be responsible for designing, optimizing, and maintaining high-performance semantic models, delivering end-to-end BI solutions, and supporting distributed reporting across multiple business domains.


Key Responsibilities:


Power BI Development & Engineering

  • Build and optimize Power BI Semantic Models for large datasets (4–5GB+).
  • Develop high-performance dashboards using Power BI Desktop & Power BI Service.
  • Write advanced, performance-optimized DAX following best practices.
  • Leverage Power Query (M) for scalable data ingestion and transformation.
  • Perform deep model optimization using Tabular Editor, DAX Studio, and performance analyzer tools.
  • Apply strong understanding of the Power BI calculation engine and performance tuning techniques.

Data Engineering & Integration

  • Design and implement robust data pipelines from Snowflake, SQL Server, SharePoint, and other enterprise systems.
  • Ensure data accuracy, consistency, and reliability across distributed reporting ecosystems.
  • Conduct data validation, quality checks, and impact assessments for model and logic changes.
  • Develop scalable tabular models and optimized reporting structures

Analytics, Reporting & Governance

  • Manage reporting across multiple teams/domains in a structured, enterprise BI environment.
  • Create clean, intuitive dashboards and wireframes aligned with business needs.
  • Perform unit testing and follow structured change management processes.
  • Support large-scale, multi-entity reporting use cases (preferred).


Required Skills & Experience:


  • 10+ years of experience in BI/Data Engineering roles.
  • Advanced expertise with: Power BI Desktop & Service, Power BI Semantic Models, DAX (advanced, optimized), Power Query (M), SQL (strong proficiency), Tabular Editor & DAX Studio
  • Experience working with large datasets and complex enterprise reporting environments.
  • Strong knowledge of data modeling principles and high-performance tabular architecture.
  • Excellent communication, problem-solving, and attention to detail.


We’re grateful for your interest in joining our team. Kindly note that only applicants whose experience and qualifications most closely align with the role will be contacted for the next steps. Thank you for your understanding.

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.