Data Engineer

Artemis Talent
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Location: London / Remote

Salary: £50,000 - £70,000 + Great Benefits package

Skills: Data Engineer, LogicApps, APIs, ETL, ETL Datalakes, ETL, pipelines, SQL, Azure, Salesforce, Data factory

Artemis Talent have partnered exclusively with a Scaling UK fintech who have an AI driven Financial Planning and Wealth Management tool. We are looking to fill a number of positions with experienced Data Engineers with 3+ years' or experience working in similar positions. Ideally the successful Data Engineer will have a strong understanding of ETL, LogicApps, APIs(Python) and Cloud(Azure). This innovative and rapidly growing organisation focuses on delivering outstanding customer experiences to customers who are looking to improve their wealth and introduce wealth management to people who have not had access before.

As a Data Engineer you will be joining their software engineering function to contribute to the solution design and implementation, while sharing responsibility for performance, and scalability, in what is a very relaxed but innovative environment.

Skills/Experience:

Strong API Python coding ability Proficiency with using Azure integrating with Saleforce in the Cloud Experience with SQL and Databases Vast experience / Knowledge of ETL / ELT processing / streaming pipelines LogicApps

You will get the opportunity to work on various different projects, integrating new systems and delivering intuitive platforms to a global audience. You have the option or remote working or using the London office.

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.