Lead Data Engineer | London, UK

Morgan McKinley
London
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Analyst

Principal Data Engineer

Contract Azure Data Engineer - £600/pd Outside IR35

About the Role:
We're looking for a Lead Data Engineer to join an innovative HealthTech company on a 6-month contract. This is your chance to play a key role in shaping AI-driven healthcare solutions, building scalable data infrastructure, and working with a team that's passionate about making a real impact.

What You'll Be Doing:

  • Designing and optimising AI-ready data pipelines and ETL processes.
  • Building scalable data solutions to support machine learning and AI-driven analytics.
  • Working closely with data scientists, AI engineers, and software teams to turn data into powerful insights.
  • Ensuring data integrity, governance, and compliance with healthcare regulations.
  • Leveraging cloud-based AI and data platforms (AWS/Azure/GCP) and cutting-edge data tools.
  • Providing technical leadership and mentoring junior engineers.


What We're Looking For:

  • Proven experience as a Lead Data Engineer in AI or machine learning environments.
  • Strong background in ETL development, data modelling, and data warehousing for AI and ML applications.
  • Proficiency in SQL, Python, or Scala, with hands-on experience in AI/ML data workflows.
  • Expertise with cloud platforms (AWS, Azure, or GCP) and AI-related services.
  • Familiarity with big data technologies like Spark, Databricks, or Kafka.
  • Knowledge of MLOps, feature engineering, and model deployment is a plus.
  • Experience in regulated industries (healthcare, finance) is a bonus.
  • Strong problem-solving skills and the ability to communicate technical ideas clearly.

#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

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.

Jobs

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.

Education

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.

🍪 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.