Data Engineering Manager

Data Science Festival
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Science Manager – Property Tech – London

BI and Data Engineering Lead

Data Science Manager - Property Tech - London

Data Science Manager – Property Tech – London

BI and Data Engineering Lead

Data Governance Manager

Location: London – Hybrid

Data Idols are proud to partner with one of the UK’s most loved retail brands going through a major data transformation.

As Data Engineering Manager, you’ll lead and inspire a talented team of data engineers through a period of exciting change – helping shape and deliver the company’s evolving data strategy and unlock real business value across the organisation.

This is a leadership-focused role (not hands-on), but your strong technical background will enable you to contribute meaningfully to architectural discussions and help unblock the team when needed.

The Opportunity
  • Leading and mentoring a team of skilled data engineers
  • Driving delivery of the data strategy in alignment with wider business goals
  • Working closely with cross-functional stakeholders across data, product, and tech
  • Providing technical oversight and helping navigate data architecture decisions
  • Fostering a high-performance, inclusive, and collaborative team environment
Skills and Experience
  • Proven experience managing high-performing Data Engineering teams
  • Strong stakeholder engagement skills across tech and non-tech teams
  • Background in Azure-based data platforms (Data Factory, Databricks, Synapse, etc.)
  • Excellent understanding of modern data architecture and engineering best practices
Why Join?

Be part of a business investing heavily in data to drive innovation and better customer experiences

Work with passionate teams in a modern, flexible working culture

Access to significant L&D support, generous perks, and the chance to make a real impact

Ready to lead something meaningful? Apply today!

Call now on 01908 465 570 or leave Maia a message.

A member of our team will be in touch shortly to arrange our chat.


#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 Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.