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

Apply Now

Lead Data Engineer

Scott Logic
Glasgow
1 week ago
Create job alert
Overview

We work with some of the UK’s biggest companies and government departments to provide a pragmatic approach to technology, delivering bespoke software solutions and expert advice. Our clients are increasingly looking to us to help them make the best use of their data. In building data platforms and pipelines, our data engineers create the foundation for diverse data & analytics solutions, including data science and AI. They build data lakes and warehouses, create the processes to extract or access operational data, and transform siloed datasets into integrated data models that allow insight into business performance and problems or training of ML models.


Role

These are hands-on, client-facing roles, with openings at senior or lead level to suit your experience. You may be leading teams, setting technical direction, advising clients or solving tough engineering challenges. You'd also be expected to spend some time on-site with clients in the London area on an ad-hoc basis.


Our data engineers combine a strong software engineering approach with solid data fundamentals and experience with modern tools. We’re technology agnostic, and we’re open minded when it comes to your existing skillset.


What we’re looking for

  • Good experience with some of the technologies and approaches typical in modern data engineering and reporting, including storage, data pipelines to ingest and transform data, and querying & reporting of analytical data.
  • You\'ve worked with technologies such as Python, Spark, SQL, Pyspark, PowerBI etc.
  • You\’ve got a background in software engineering, including Front End technologies like JavaScript.
  • You’re a problem-solver, pragmatically exploring options and finding effective solutions.
  • An understanding of how to design and build well-structured, maintainable systems.
  • Strong communication skills and a collaborative approach to work.
  • You embrace the chance to try new things, learn new skills and grow your experience.

Nice-to-have

  • Experience of relevant cloud services within AWS, Azure or GCP.
  • Experience working in an Agile environment.
  • Experience working with common vendor products such as Snowflake or Data Bricks.
  • Experience working with CI/CD tooling.

What you’ll get in return

  • 25 days’ annual leave, rising to 30 days with each year of service.
  • Generous family leave policies.
  • Access to an employer pension scheme, private medical services and Group Life Assurance.
  • A range of optional benefits such as discounted gym membership and a cycle-to-work scheme.
  • A meaningful approach to evaluating your performance and providing feedback on your progress.

About us

At Scott Logic, we value the flexibility of remote working alongside the value gained from spending time with our colleagues and clients. In our offices you’ll find employee-led clubs and events, as well as free games, books, and refreshments.


We have shared values that govern our behaviour toward others and the environment. We are proud to be a B Corp, a global movement of businesses driving for a more inclusive, equitable, and regenerative economy. We believe diversity drives innovation, and embrace a culture where everyone can contribute, irrespective of race, religion, colour, national origin, gender, sexual orientation, age, marital status or disability.


#J-18808-Ljbffr

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer - Edinburgh

Lead Data Engineer

Lead Data Engineer

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.