Data Engineer

UNITING PEOPLE LTD
London
1 week ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Location

Bournemouth
5 Days ONSITE
FTE/FTC - £80k
Start Date

ASAP
Data Engineer with AIML(LLM, Agentic AI) & Python experience
AIML, Machine Learning & Data Science.
Large Language Models(GPT, Claude), Generative AI, Retrieval Augmented Generation.
Agentic AI, CoPilot, MCPs.
AIML Algorithms(Regression, Classification, Decision Trees, KNN, K-Means)
Python (NLTK, NumPy, Scikit-learn, Pandas)
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.
The role will require proficiency in all aspects of AIML & Software Development including:
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.
Bachelors 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
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.

Data Engineering Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data engineering in your 30s, 40s or 50s? You’re not alone. In the UK, companies of all sizes — from fintechs to government agencies, retailers to healthcare providers — are building data teams to turn vast amounts of information into insight and value. That means demand for data engineering talent remains strong, but there’s a gap between media hype and the real pathways available to mid-career professionals. This guide gives you the straight UK reality check: which data engineering roles are genuinely open to career switchers, what skills employers actually look for, how long retraining really takes and how to position your experience for success.

How to Write a Data Engineering Job Ad That Attracts the Right People

Data engineering is the backbone of modern data-driven organisations. From analytics and machine learning to business intelligence and real-time platforms, data engineers build the pipelines, platforms and infrastructure that make data usable at scale. Yet many employers struggle to attract the right data engineering candidates. Job adverts often generate high application volumes, but few applicants have the practical skills needed to build and maintain production-grade data systems. At the same time, experienced data engineers skip over adverts that feel vague, unrealistic or misaligned with real-world data engineering work. In most cases, the issue is not a shortage of talent — it is the quality and clarity of the job advert. Data engineers are pragmatic, technically rigorous and highly selective. A poorly written job ad signals immature data practices and unclear expectations. A well-written one signals strong engineering culture and serious intent. This guide explains how to write a data engineering job ad that attracts the right people, improves applicant quality and positions your organisation as a credible data employer.

Maths for Data Engineering Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data engineering jobs in the UK, maths can feel like a vague requirement hiding behind phrases like “strong analytical skills”, “performance mindset” or “ability to reason about systems”. Most of the time, hiring managers are not looking for advanced theory. They want confidence with the handful of maths topics that show up in real pipelines: Rates, units & estimation (throughput, cost, latency, storage growth) Statistics for data quality & observability (distributions, percentiles, outliers, variance) Probability for streaming, sampling & approximate results (sketches like HyperLogLog++ & the logic behind false positives) Discrete maths for DAGs, partitioning & systems thinking (graphs, complexity, hashing) Optimisation intuition for SQL plans & Spark performance (joins, shuffles, partition strategy, “what is the bottleneck”) This article is written for UK job seekers targeting roles like Data Engineer, Analytics Engineer, Platform Data Engineer, Data Warehouse Engineer, Streaming Data Engineer or DataOps Engineer.