Engineer the Quantum RevolutionYour expertise can help us shape the future of quantum computing at Oxford Ionics.

View Open Roles

Lead Data Engineer

Scott Logic
Glasgow
1 week ago
Create job alert

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.

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.

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 are we 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. 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. 

It would be great if you 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 is:

25 days’ annual leave, rising incrementally to 30 days after six years 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.

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.

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer...

Lead Data Engineer (Only 24h Left)...

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.

The Future of Machine Learning Jobs: Careers That Don’t Exist Yet

Machine learning (ML) has quickly become one of the most transformative forces in modern technology. What began as a subset of artificial intelligence—focused on algorithms that learn from data—has grown into a foundational capability across industries. From voice assistants and recommendation systems to fraud detection and predictive healthcare, machine learning underpins countless innovations shaping daily life. In the UK, demand for ML professionals has surged. Financial services, healthcare providers, retailers, and tech start-ups are investing heavily in ML talent. Roles like Machine Learning Engineer, Data Scientist, and AI Researcher are among the most sought-after and best-paid in the tech sector. Yet we are still only at the start. Advances in generative AI, quantum computing, edge intelligence, and ethical governance are reshaping the field. Many of the most critical machine learning jobs of the next 10–20 years don’t exist yet. This article explores why new careers will emerge, the kinds of roles likely to appear, how today’s jobs will evolve, why the UK is well positioned, and how professionals can prepare.

Seasonal Hiring Peaks for Machine Learning Jobs: The Best Months to Apply & Why

The UK's machine learning sector has evolved into one of Europe's most intellectually stimulating and financially rewarding technology markets, with roles spanning from junior ML engineers to principal machine learning scientists and heads of artificial intelligence research. With machine learning positions commanding salaries from £32,000 for graduate ML engineers to £160,000+ for senior principal scientists, understanding when organisations actively recruit can dramatically accelerate your career progression in this pioneering and rapidly evolving field. Unlike traditional software engineering roles, machine learning hiring follows distinct patterns influenced by AI research cycles, model development timelines, and algorithmic innovation schedules. The sector's unique combination of mathematical rigour, computational complexity, and real-world application requirements creates predictable hiring windows that strategic professionals can leverage to advance their careers in developing tomorrow's intelligent systems. This comprehensive guide explores the optimal timing for machine learning job applications in the UK, examining how enterprise AI strategies, academic research cycles, and deep learning initiatives influence recruitment patterns, and why strategic timing can determine whether you join a groundbreaking AI research team or miss the opportunity to develop the next generation of machine learning algorithms.

Pre-Employment Checks for Machine Learning Jobs: DBS, References & Right-to-Work and more Explained

Pre-employment screening in machine learning reflects the discipline's unique position at the intersection of artificial intelligence research, algorithmic decision-making, and transformative business automation. Machine learning professionals often have privileged access to proprietary datasets, cutting-edge algorithms, and strategic AI systems that form the foundation of organizational competitive advantage and automated decision-making capabilities. The machine learning industry operates within complex regulatory frameworks spanning AI governance directives, algorithmic accountability requirements, and emerging ML ethics regulations. Machine learning specialists must demonstrate not only technical competence in model development and deployment but also deep understanding of algorithmic fairness, AI safety principles, and the societal implications of automated decision-making at scale. Modern machine learning roles frequently involve developing systems that impact hiring decisions, financial services, healthcare diagnostics, and autonomous operations across multiple regulatory jurisdictions and ethical frameworks simultaneously. The combination of algorithmic influence, predictive capabilities, and automated decision-making authority makes thorough candidate verification essential for maintaining compliance, fairness, and public trust in AI-powered systems.