Data Engineer

Computappoint
Preston
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

IT Data Engineer – Permanent – Hybrid – Preston, Lancashire

  • Salary: Up to £60,000 per annum (DOE)
  • Location: Preston, Lancashire

About the Client and Role

Our client is a commercially focused business and leader in the property and construction industry. They are seeking an experienced IT Data Engineer to play a key role in shaping their data usage throughout the complete construction project lifecycle, from initial estimation through to procurement, on-site operations, safety, commercial performance, finance, and asset management. You will establish the modern data foundations that empowers their teams to make reliable, timely, insight-driven decisions.


Key Responsibilities

  • Design and deliver end‑to‑end data solutions using Microsoft Fabric (including Lakehouse, Warehousing, Dataflows, and Notebooks) that support both enterprise-wide architecture and project-specific needs.
  • Develop reliable ELT/ETL pipelines that integrate data from essential construction systems such as ERP, finance, procurement, project controls, BIM/CDE platforms, and site health & safety applications.
  • Build and maintain high‑quality semantic models for Power BI, creating reusable measures, KPIs, and hierarchies whilst ensuring optimal performance and consistent business definitions.
  • Establish strong governance practices in Fabric, including workspace design, Lakehouse organisation, medallion architecture, data security, lineage, and sensitivity labelling.
  • Implement CI/CD pipelines to automate deployment of data assets from notebooks and pipelines through to Lakehouse tables and Power BI datasets and reports.
  • Hands‑on experience with Microsoft Fabric including Lakehouse/Warehouse, Dataflows, Pipelines, and Notebooks (PySpark).
  • Expert Power BI skills across data modelling (star schema), DAX, performance optimisation, RLS, composite models, and deployment pipelines.
  • Strong data engineering foundations: ELT/ETL design, orchestration, schema design, data quality, and observability.
  • Proficiency in SQL for transformations and optimisation, plus Python/PySpark for data processing.
  • Experience integrating data from systems such as ERP/finance (e.g., Business Central), scheduling tools (Primavera/MSP), BIM/CDE platforms (Autodesk/BC), and APIs/flat files.
  • Practical knowledge of Git and ideally CI/CD for Fabric and Power BI assets.
  • Comprehensive understanding of data governance and security, including privacy, sensitivity labelling, RLS/OLS.
  • Confident working with business stakeholders, translating domain requirements into clear technical solutions.
  • Ability to create high‑quality documentation, including data contracts, mappings, design decisions, and runbooks.

Services offered by Computappoint Limited are those of an Employment Business and/or Employment Agency in relation to this vacancy.


Computappoint do not use AI to filter or assess candidates, we use experienced and dedicated recruiters, who want to match the best people to roles.


#J-18808-Ljbffr

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.

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

Advertising data science jobs in the UK requires a different approach to most technical hiring. 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.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.