Data Engineer

Fenny Stratford
11 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer – Cloud & Data Transformation

Location: Milton Keynes (Hybrid) or remote working / home based within the UK with very occasional office visits

Are you a Data Engineer passionate about cloud data solutions, modern data architectures, and scalable ETL/ELT processes? Join a forward-thinking organisation in the financial services sector, driving data transformation and innovation.

About the Role

As a Data Engineer, you will play a key role in developing and optimising data pipelines, integrating structured and unstructured data, and supporting the evolution of a modern Snowflake cloud-based data platform. This role involves working with Snowflake, data lakehouse architectures, and cloud technologies to ensure robust, scalable, and efficient data processing.

Key Responsibilities

  • Develop and maintain ETL/ELT processes to ingest, transform, and integrate data from multiple sources into Snowflake and data lakehouse environments

  • Design and optimise data models and schemas for both structured and unstructured data with a snowflake environment

  • Enhance cloud data capabilities, working with AWS S3, Azure Data Lake, and Apache Spark

  • Collaborate with cross-functional teams to ensure data availability for advanced analytics and reporting

  • Automate workflows, implement CI/CD pipelines, and maintain data integrity and quality

  • Stay ahead of emerging technologies to drive continuous improvement and innovation in data engineering

    Key Skills & Experience

  • Proven experience in data engineering, data warehousing, and data lakehouse development

  • Hands-on expertise in Snowflake and cloud platforms (AWS, Azure)

  • Strong SQL and programming skills in Python, Java, or Scala

  • Experience with data integration tools, APIs, and real-time data processing

  • Knowledge of BI tools (Tableau, Power BI) and ETL/ELT frameworks (Talend preferred)

  • Excellent problem-solving and stakeholder communication skills

    Why Join Us?

  • Work on cutting-edge cloud and data projects in a fast-moving, innovative environment

  • Be part of a collaborative, inclusive, and forward-thinking team

  • Career growth opportunities, training, and professional development

  • Influence the future of data strategy and analytics within a leading organisation

    Benefits:

  • Competitive salary

  • 10% bonus

  • Excellent 10.5% company pension contribution

  • Comprehensive healthcare package

  • Flexible working arrangements - Milton Keynes (Hybrid) or remote working / home based within the UK with adhoc and occasional office visits

  • Professional development opportunities

  • Modern tech stack and innovation-focused environment

  • 27 days annual leave (plus bank holidays) and a holiday purchase scheme

  • Life Assurance (x4 salary), Subsidised private medical insurance, Cycle to Work scheme, Employee discounts platform, including gym discounts, 24/7 employee assistance programme supporting your mental wellbeing, 2 days volunteer leave, etc

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.