Senior Electronics Engineer

Buckingham
8 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Scientist

Senior Data Engineer

Senior Data Engineer (AWS, Airflow, Python)

Senior Data Governance Analyst

Senior Data Engineer

Senior Electronics Engineer – Buckingham - £65k - £75k

Hexwired Recruitment has partnered with a world renowned Electronics manufacturer based in Buckingham who are now seeking a Senior Electronics Engineer with solid High Digital design experience, to help develop their latest range of products.

The company are recognised globally, and are expanding because of a brand new portfolio of products the company is looking to develop. The company are now seeking a Senior Electronics Engineer, ideally with experience working across the Full product lifecycle for Mixed signal design.

This is a Senior Electronics Engineer role, primarily hands on and you will be the main contributor for a brand new product the company is developing. Due to the nature of the work, this will be a mostly onsite role with occasional remote working.

Key Requirements:

  • Bachelors, Masters or PhD in Electronics, Embedded Systems, Maths, Physics or similar

  • 5+ years commercial engineering background (Mixed Signal design preferred preferred)

  • Solid knowledge of PCB design experience using Altium

  • Experience working on RF or FPGA based products is highly desirable

  • The ability to gain Security clearance

    The company are looking to offer circa £75k dependent on experience. Along with an excellent benefits package. If you’re interested in this Senior Electronics Engineer role, please apply.

    For more information on this role or any other jobs across; FPGA, Mixed-signal, Electronics, Hardware, Embedded, C++ programming, Embedded Linux, Golang Development, Machine Learning, Data Science or Simulation contact us today

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.