Data Engineer - Defence

IBM
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer – Defence

IBM CIC delivers deep technical and industry expertise to a wide range of public and private sector clients in the UK, and this role offers you the opportunity to work with visionaries across multiple industries to improve the hybrid cloud and AI journey for innovative and valuable companies worldwide.


We Offer

  • Many training opportunities, from classroom to e‑learning, mentoring, coaching and industry‑certified programmes.
  • Regular promotion opportunities and career development support.
  • Feedback and checkpoints throughout the year.
  • Diversity & inclusion as an essential component, supported by employee champion teams.
  • An inclusive culture where growth and innovation ideas are welcomed.
  • Internal recognition programmes.
  • Work‑life balance tools: flexible working, sabbatical, paid paternity, maternity leave and a maternity returner scheme.
  • Traditional benefits: 25 days holiday plus public holidays, online shopping discounts, Employee Assistance Programme, group personal pension plan with extra 5 % of base salary paid monthly.

Your Role and Responsibilities

Managing Data Engineer – Advanced Analytics. Lead the development of innovative advanced analytics solutions, nurture junior engineers, and drive continuous improvement.


Responsibilities

  • Develop and lead cutting‑edge analytics solutions for complex business problems.
  • Mentor junior data engineers and support their professional growth.
  • Perform statistical, data mining and text mining analyses.
  • Design, build and manage solutions for advanced analytics projects.
  • Utilise predictive analytics tools (e.g. SPSS) to draw conclusions and present findings.
  • Stay abreast of emerging analytics trends and technologies to drive innovation.

Preferred Education

Bachelor’s Degree


Required Technical And Professional Expertise

  • Extensive experience with data engineering principles and advanced analytics techniques.
  • Proficiency in Python, R and SQL.
  • Experience with Pandas, NumPy and Dask.
  • Strong leadership and communication skills.
  • Ability to lead cross‑functional teams and manage stakeholder expectations.

Legal and Eligibility

Equal opportunities employer. Applicants must have a valid right to work in the UK, a continuous UK residency of at least 10 years and be able to obtain or hold a UK government security clearance. No visa sponsorship is offered.


Preferred Technical And Professional Experience

  • Experience with machine learning frameworks (TensorFlow, PyTorch, scikit‑learn).
  • Familiarity with big data technologies (Hadoop, Spark).
  • Background in data science, IT consulting or related field.
  • AWS Certified Big Data or equivalent.

Location

London, England, United Kingdom


Seniority Level

Mid‑Senior level


Employment Type

Full‑time


Job Function

Information Technology


Industries

IT Services and IT Consulting


#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.