Data Engineer

Dabster
Bournemouth
2 months ago
Create job alert

Dabster Bournemouth, England, United Kingdom


About Us

At Dabster, we specialize in connecting top talent with leading global companies. We are currently seeking a skilled and dedicated Data Engineer to join our client's team in Bournemouth, UK (Onsite). Our mission is to be the foremost recruitment specialist in securing exceptional talent for a diverse range of global clients.


Who You Will Work With

Our client is a globally recognized technology company delivering IT services, consulting, and business solutions. They partner with leading organizations worldwide to drive digital transformation, leveraging innovation and deep industry expertise to solve complex business challenges.


Job Description

  • Large Language Models (GPT, Claude), Generative AI, Retrieval Augmented Generation.
  • Agentic AI, CoPilot, MCPs.
  • AIML Algorithms (Regression, Classification, Decision Trees, KNN, K‑Means).

Candidates will be expected to work on developing & implementing AIML Solutions for Test Automation in the Securities Processing space. This will entail building AIML Solutions for Test Generation, Test Prioritization, Defect Triage/Reporting, Code Coverage, Framework Migration/Setup. The role requires experience in AIML (LLMs, Gen AI & Agentic AI) & Python.


Qualifications

  • Knowledge of AIML & Python is must.
  • Ability to develop and implement Generative AI & Retrieval Augmented Generation solutions focused on software testing.
  • Experience with Large Language Models (GPT, Claude).
  • Hands‑on experience with GitHub Copilot.
  • Must be a regular user of Agentic AI solutions and MCPs.
  • Deployment experience with Docker & Kubernetes to deploy the AIML solutions is good to have.
  • Front End experience in React to build front end for the AIML solutions is a plus.
  • Hands‑on experience with Python libraries like NLTK, NumPy, Scikit‑learn, Pandas.
  • Knowledge of AIML algorithms (Regression, Classification, Decision Trees, KNN, K‑Means) is preferred.
  • Experience with building, training & finetuning AIML models is a plus.
  • Bachelor’s degree in Computer Science or related field of study or equivalent relevant experience; demonstrated experience of Data Science & AIML with focus on quality assurance solutions.
  • Lifecycle principles and quality assurance processes and methodologies.
  • Experience with automated testing with good understanding of test automation frameworks.
  • Good grasp of SQLs.
  • Experience of working in an Agile environment, participating in sprint planning, backlog refinement, and retrospectives.
  • Must have excellent verbal and written skills being able to communicate effectively on both a technical and business level.

How to Apply

Apply by submitting your resume today, showcasing your relevant experience and passion for the position via LinkedIn Easy Apply or directly to .


Seniority Level

Mid‑Senior level


Employment Type

Full‑time


Job Function

Information Technology


Industries

Technology, Information and Media


Referrals increase your chances of interviewing at Dabster by 2x


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

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.

New Data Engineering Employers to Watch in 2026: UK and Global Companies Driving the Data Revolution

Data engineering is at the heart of the digital economy, transforming raw data into actionable insights, powering analytics, AI systems, and cloud infrastructure. As the UK and global markets continue to invest heavily in data platforms, pipelines, and real-time analytics, demand for skilled data engineers is growing rapidly. For professionals exploring opportunities on www.DataEngineeringJobs.co.uk , the critical question is: which companies are expanding, hiring, and shaping the future of data-driven business? This article highlights new data engineering employers to watch in 2026, including UK startups, scale-ups, and international firms expanding in the UK.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.