Data Engineering Lead

Corecom Consulting
York, YO1 8RS, United Kingdom
Last month
Applications closed

Related Jobs

View all jobs

Finance Data Engineering Lead

Experis London, United Kingdom
£750 – £950 pd Hybrid

Data Platform Manager

Deerfoot Recruitment Solutions Luton, Bedfordshire, United Kingdom
£70,000 pa Hybrid

Data Analyst Business System Analyst

Randstad Technologies Recruitment London, City And County Of the City Of London, United Kingdom
£60,000 – £70,000 pa Hybrid

Data Scientist

Belcan Larne, County Antrim, United Kingdom
On-site

Data Delivery Lead

VIQU IT Clerkenwell, London, EC1R 0EA, United Kingdom
£90,000 – £100,000 pa Hybrid

Commercial Data Scientist

Synthesia London, United Kingdom
Remote
Seniority
Lead
Posted
17 Apr 2026 (Last month)

Data Engineering Lead / York (hybrid) / £75k-£90k

We're hiring a Data Engineering Lead to take ownership of a growing Azure-based data platform, working as part of a small, high-impact team.

This is a hands-on technical leadership role focused on building, improving, and stabilising data pipelines and architecture - while also supporting a broader BI environment.

What do we need from you?

Strong experience with Azure Data Factory and the Microsoft data stack

Solid SQL and Python skills

Experience building and maintaining data pipelines and integrations

Ability to work in a hands-on, delivery-focused role

Comfortable operating in a small team with high ownership

Understanding of modern data platforms (Fabric exposure desirable)

Role overview

You'll take ownership of the data engineering layer, responsible for ingesting and transforming data from multiple sources into a large-scale reporting environment.

The platform currently combines:

Azure Data Factory pipelines

A large Power BI estate

A legacy warehouse (partially outsourced)There is a clear roadmap toward modernisation and migration (Fabric), and you'll play a key role in shaping that journey.

Key focus areas

Build and maintain robust data pipelines

Manage data ingestion from multiple sources

Support and optimise data flows feeding Power BI

Work closely with BI and business teams to deliver data solutions

Improve platform stability, performance, and scalability

Contribute to future platform modernisation and migration

Help reduce reliance on legacy and outsourced componentsWhy join?

High ownership in a small, agile team

Opportunity to work across engineering and platform design

Involvement in a major platform transformation (Fabric)

Real impact on business-critical data systems

A role where you'll build, fix, and improve - not just maintain

If you're a hands-on Lead Data Engineer who enjoys building scalable platforms and taking ownership of delivery, apply now with a CV to Dominic Brown on

Data Engineering Lead / York (hybrid) / £75k-£90k

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Where to advertise data science jobs UK in 2026: the specialist boards, communities and channels that actually reach senior and lead data science talent. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Data Science Jobs UK 2026: What to Expect Over the Next 3 Years

Data Science Jobs UK 2026: roles, salaries and the trends shaping UK data science hiring over the next three years — from MLE crossover to GenAI workflows. Data science has spent the past decade being described as the sexiest job of the twenty-first century. By 2026, the reality is both more nuanced and more interesting than that label ever suggested. The discipline has matured, fragmented, deepened, and in some respects reinvented itself — and the jobs market has changed with it in ways that create genuine opportunity for those who understand what employers actually want, and genuine difficulty for those still operating on assumptions formed five years ago. The data science jobs market of 2026 is not simply a larger version of what it was three years ago. The generalist data scientist — equally comfortable wrangling data, building models, and presenting insights to the board — is giving way to a more specialised landscape where employers know exactly what problem they are trying to solve and are looking for candidates with the specific depth to solve it. Machine learning engineering, causal inference, experimentation, AI product development, and domain-specific applied science have all emerged as distinct career tracks within what was previously a single, loosely defined profession. At the same time, the arrival of large language models and the broader AI capability wave has both threatened and created data science roles in equal measure. Some of the work that junior data scientists spent their early careers doing — data cleaning, exploratory analysis, basic model building — is being partially automated by AI tooling. But the demand for practitioners who can evaluate AI systems rigorously, apply statistical thinking to complex business problems, and build the data foundations on which AI depends has grown considerably. The candidates who will thrive over the next three years are those who understand where the discipline is heading — which specialisms are attracting the most investment, which technologies are reshaping what data scientists are expected to build and know, and how to position a data science career that will remain valuable as the field continues to evolve around them. This article breaks down what the UK data science jobs market is likely to look like through to 2028 — covering the titles emerging right now, the technologies driving employer demand, the skills that will matter most, and how to position your career ahead of the curve.