Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer

Intellect Group
Cambridge
5 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Job Title:Mid-Level Data Engineer

Location:Fully Remote (UK-based applicants only, with optional weekly co-working in Cambridge)

Employment Type:Full-Time, Permanent

Sector:Data & AI Consultancy – Banking & Video Gaming

Salary:Competitive, dependent on experience


About the Role


Intellect Group is delighted to be recruiting on behalf of a specialist data consultancy based in Cambridge, renowned for delivering high-impact solutions across theBankingandVideo Gamingsectors. Their areas of expertise includeDigital Transformation,Machine Learning & AI,Data Engineering, andData Science.


As they continue to grow, they are now looking for aMid-Level Data Engineerto join their close-knit team. This is a fantastic opportunity for a technically strong and motivated individual with a few years of experience under their belt, who’s ready to take ownership of their work, contribute to complex projects, and work directly with clients in a variety of industries.


This role isfully remote, with the option of joining the team once a week inCambridgefor collaborative working and professional development.


Key Responsibilities


  • Design, build and optimise scalable and robust data pipelines and architectures
  • Develop and maintain ETL workflows using modern tooling
  • Contribute to solution design and technical delivery across multiple client projects
  • Collaborate closely with data scientists, analysts, and consultants to support ML/AI deployment
  • Integrate data from a variety of cloud and on-premise sources
  • Participate in internal code reviews, architecture discussions, and knowledge sharing
  • Engage with clients to understand requirements and translate them into technical solutions


Candidate Profile


  • 3–7 years of experience as a Data Engineer (or in a similar role)
  • Strong programming skills inPythonand working knowledge ofSQL
  • Solid understanding of data modelling, data warehousing, and ETL best practices
  • Exposure to bothAWSandGoogle Cloud Platform (GCP)
  • Comfortable working independently and collaborating within a distributed team
  • Excellent communication and stakeholder engagement skills
  • UK-based with the option to join weekly co-working days in Cambridge


Nice to Have


  • Experience working within aconsultancy or client-facing environment
  • Familiarity with tools and frameworks such as:
  • Databricks
  • PySpark
  • Pandas
  • Airflowordbt
  • Experience deploying solutions using cloud-native services (e.g., BigQuery, AWS Glue, S3, Lambda)


What’s On Offer


  • Fully remote working with the flexibility to work from anywhere in the UK
  • Optional weekly in-person collaboration inCambridge
  • Frequent team socials and company trips – previous destinations includeItalyand thePeak District
  • 6% pension contribution
  • Friendly, talented team culture with a strong emphasis on knowledge-sharing
  • Exposure to cutting-edge data projects across highly dynamic sectors

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 Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.