Senior Frontend Developer

ECOM
1 year ago
Applications closed

Related Jobs

View all jobs

Data Architect

Senior Data Analyst

Senior Data Engineer

Senior Data Governance Analyst

Senior Data Engineer

Senior Data Engineer (AWS, Airflow, Python)

Senior Frontend Developer (React.Js) - Manchester, £63k + benefitsECOM are supporting a long-standing company on their search for a Senior Frontend Developer for their Manchester based team. The client are an already established and growing eCommerce company in Manchester that have ambitions to become Europe’s leading online retailer in their industry.The technology department is evolving to cross functional product-based teams that use data to inform decisions and as a Senior Frontend Engineer, you’d be responsible for technically leading your team towards a cloud based, micro-frontend architecture and continuous delivery environment.The current team is collaborative and cross functional, they value diversity and teamwork over individual heroics, embrace an agile mindset and use data to inform their decisions so you can expect to work alongside product owners, designers, testers, data engineers and other software engineers that are passionate about using quality practices like TDD, pair programming and DevOps.Tech:- TypeScript (React, Next, Node)- AWS, Kubernetes- Agile, DevOps and TDDOur client are recruiting across the levels and offer an excellent progression plan that enables employees to increase their salary year on year. For this one the starting salary is up to £63,000 plus excellent progression opportunities, company benefits and the option to regularly work from home.If you are interested in this opportunity or would like to know more please APPLY NOW and I will be in touch ASAP.

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.