Data Engineer

Coaction Recruitment Limited
Liverpool
2 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer
£60,000 to £75,000 basic salary per annum plus an excellent benefits package including bonus, pension, 25 days holiday (can buy up to 10 additional days), two wellness days, two volunteering days, healthcare scheme, excellent career development plans (courses & certifications), hybrid working (12 days per week in the office), etc.
Our client, a leading UK law firm ranked as one of the best companies to work for in the country, is seeking a Data Engineer to join their small AI team on a permanent basis. This is a fantastic opportunity to join an innovative law firm where the utilisation of AI has become fundamental to their business strategy.
You will play a key role in building a brand-new AI-enabled data platform while also contributing to the development of the AI-driven products it powers. The successful Data Engineer will split their time between core data engineering tasks and AI product development.
Essential skills:
At least 34 years of Data Engineering experience
Proven experience leading data projects
Experience working across the full project lifecycle
SQL
Python
Experience with cloud-based data platforms
Any demonstrable experience with AI (professional or personal)
This is an excellent opportunity for a Data Engineer to join an organisation where employee wellbeing is paramount and career progression is heavily supported. This role will suit someone who enjoys variety in their day-to-day work and has ideally been involved in the build of a brand-new data platform previously.
If you are interested, please click the apply now button.
Add me on Linked In to stay up to date with new opportunities - search Ollie Cottrill and youll easily find me.
Coaction Recruitment Ltd acts as an employment agency for permanent recruitment and an employment business for the supply of temporary workers.

TPBN1_UKTJ

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.