Principal Data Engineer

Shaw Daniels Solutions
Glasgow
1 month ago
Applications closed

Related Jobs

View all jobs

Principal Data Engineer

Principal Data Engineer (GCP)

Principal Data Engineer (MS Azure)

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

Location: United Kingdom

Our Client

They are a global content solutions company, powered by technology and human expertise. They help organisations grow the value of their ideas, data, and content by enabling them to be understood - everywhere.

With over 45 AI patents, industry-leading technology, and expert talent, they support more than 80 of the world’s top 100 brands. Their teams operate across 60+ locations worldwide, delivering solutions that accelerate time-to-market, deepen customer engagement, and unlock new global opportunities.

Role Overview

As Principal Data Engineer, you will be a senior technical leader responsible for shaping the architecture, scalability, and evolution of the enterprise data platform.

This role goes far beyond delivery. You will define technical strategy, design scalable data systems, mentor senior engineers, and partner closely with product, engineering, analytics, and business leaders to transform strategic goals into robust, data-driven solutions.

You will have significant autonomy to influence architectural decisions, introduce best practices, and create a lasting technical legacy across the global data ecosystem.

Key Responsibilities

  • Lead the design and evolution of scalable, secure, and reliable data architectures supporting BI, analytics, and ML workloads.
  • Define and drive data engineering standards, architecture patterns, and best practices across teams.
  • Influence technical strategy across engineering, product, and analytics through clear communication and stakeholder collaboration.
  • Anticipate future data needs, balancing long-term vision with pragmatic delivery.
  • Own reliability, performance, and observability of critical data systems, including SLO definition and operational resilience.
  • Mentor and develop senior data engineers through architecture reviews, technical coaching, and knowledge sharing.
  • Champion data quality, governance, monitoring, incident response, and cost optimisation.
  • Evaluate emerging tools and technologies, guiding adoption where they deliver genuine business value.

Skills & Experience

  • Extensive experience designing and operating large-scale, cloud-based data platforms.
  • Deep expertise in data engineering fundamentals: data modelling, pipeline orchestration, distributed systems, and performance optimisation.
  • Strong architectural judgement, balancing technical excellence with business outcomes.
  • Proven leadership and influence across cross-functional teams.
  • Demonstrated success mentoring senior engineers and scaling team effectiveness.
  • Exceptional communication skills, including technical writing, documentation, and executive presentations.
  • Strong business acumen with the ability to connect technical decisions to measurable ROI.


Desirable Experience

  • Hands-on experience with Google Cloud Platform (Big Query, Data form, Cloud Composer).
  • Metadata management and data cataloguing (e.g., DataHub, Alation).
  • Modern orchestration platforms (Airflow, Prefect, Dagster).
  • Cloud-native architectures, including Kubernetes, serverless, and Infrastructure as Code.
  • Data mesh or decentralised data architecture implementations.
  • Model Context Protocol (MCP) for data assets.
  • Open-source contributions or active participation in the data engineering community.

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.

Careers

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.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.