Back End Developer

NearTech Search
Liverpool
1 year ago
Applications closed

Related Jobs

View all jobs

Scala Data Engineer

Scala Data Engineer

Data Engineer (Scala)

Data Engineer (Scala)

Scala Data Engineer

Scala Data Engineer

Senior Full Stack Engineer (Backend Focus)

Scroll down for a complete overview of what this job will require Are you the right candidate for this opportunityMy client is seeking a highly skilled

Senior Full Stack Engineer

with a strong focus on backend development to join their innovative team. They are looking for a professional with

8+ years of development experience

who is passionate about building robust, scalable systems for

SaaS/DaaS platforms

and has a proven track record of delivering high-quality, impactful solutions.This role will involve modernising legacy systems, developing APIs and services, and contributing to Greenfield projects that empower clients to make informed decisions while addressing key challenges in sustainability.Responsibilities:Lead the design and development of scalable, backend-heavy systems with a focus on performance and maintainability.Build and optimise services and APIs, ensuring they meet both current and future needs.Collaborate with cross-functional teams to deliver innovative solutions for SaaS/DaaS platforms.Mentor and guide junior engineers, promote best practices, and conduct detailed code reviews.Drive improvements in software development processes, focusing on CI/CD, security, and scalability.Requirements:8+ years of development experience , with a strong emphasis on

C#/.NET

in building APIs, services, and scalable backend solutions.Proficiency in

TypeScript

and familiarity with service-oriented architectures.Experience working with cloud platforms such as

Azure

or

AWS , and deploying containerised applications using

Docker

and

Kubernetes .Strong understanding of database design, optimisation, and management (SQL/NoSQL).A structured and semantic problem-solving approach, with the ability to remain flexible and collaborative.A background in

SaaS/DaaS platforms , ideally in B2B environments, working on service-heavy solutions.Desirable Skills:Familiarity with JavaScript frameworks and the ability to contribute to frontend-related tasks when necessary.Experience migrating legacy, on-premise systems to modern cloud-native solutions.Knowledge of modern data engineering practices.What’s on Offer:My client provides an exceptional working environment that blends purpose with flexibility. They support remote work but value the benefits of in-person collaboration during key meetings. The role comes with a competitive benefits package, including:Private healthcare with added perks like Headspace membership.Generous annual leave, increasing with tenure.Enhanced life insurance, income protection, and parental leave.Employer pension contributions up to 5%.A strong commitment to diversity, equity, and inclusion in the workplace.Next Steps:If you’re an experienced developer ready to bring your expertise to an impactful role, I’d love to discuss this opportunity with you. My client is focused on creating innovative solutions that make a difference – join them to shape the future of the industry.The role is remote first, candidates are expected to travel to offices in the midlands

once

a month.Please note: Applicants must have the right to work in the UK.

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.