Principal Data Engineer (Core Engineering)

Royal London Group
Edinburgh
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Simulation Engineer (Data Science)

Principal Data Scientist

Data Science Lead / Manager

Data Science Manager

Senior Machine Learning Engineer

Principal Machine Learning Scientist - Applied Research (UK Remote)

Job Title: Principal Data Engineer

Contract Type: Permanent

Location: Alderley or Edinburgh or Glasgow

Working style: Hybrid 50% home/office based

Closing date: 29th October 2024

 

Royal London is seeking a Principal Data Engineer to lead a Core Engineering team responsible for the build out of the Enterprise Data Platform (EDP). The EDP is a modern Data Lakehouse implementation on Azure Databricks and is central to the Group’s data strategy and consists of a core structured data warehouse area plus an attached data science\ML platform.

 

This role presents a fantastic opportunity for an experienced data engineer – helping setup the core engineering team (who consist of data engineers, testers, and data modellers), defining reusable frameworks, patterns, and standards, guiding the data modellers in the definition and extension of the silver layer data model, and managing Agile Engineering boards/backlogs.

 

About the Role

 

  • Design and implement Extract, Transform, and Load (ETL) processes across a Data Lakehouse to ensure efficient data movement with appropriate transformations. Define and publish data engineering and ETL standards for the data platform.
  • Analyse and communicate complex data engineering problems to senior audiences. Work with both structured and unstructured data to support downstream business outcomes. Oversee data movement across platforms, including designing controls and technical checks for monitoring data pipelines.
  • Work with Databricks, Lakehouse, ETL, and data pipelines.
  • Provide technical leadership, responsible for mid and lower-level designs, patterns, and standards. Produce artifacts for architecture and design authorities to make informed decisions.

 

About You

 

  • Extensive technical design and development experience in specialism for the platform. Expected to be a code contributor in specialism.
  • Experience with Azure Databricks, Azure Data Factory.
  • Proficiency in SQL, Python, and PySpark.
  • Knowledge of Azure data lake, ADLS Gen 2 storage, and Azure services.
  • Extensive experience with Data Warehousing, ETL concepts, and data structure designs, including data pipelines and data marts (Inmon & Kimball approaches).
  • A broad understanding of BI and analytics tools, such as PowerBI, and optimizing consumption against engineered data layers.
  • Broad knowledge within wider and adjacent data domains, for example, ML/AI, Master Data Management (MDM), data analysis, and data modeling.

 

About Royal London

 

We’re the UK’s largest mutual life, pensions, and investment company, offering protection, long-term savings and asset management products and services.   

 

OurPeople Promiseto our colleagues is that we will all work somewhere inclusive, responsible, enjoyable and fulfilling. This is underpinned by our Spirit of Royal London values; Empowered, Trustworthy, Collaborate, Achieve. 

 

We've always been proud to reward employees by offering great workplace benefits such as 28 days annual leave in addition to bank holidays, an up to 14% employer matching pension scheme and private medical insurance. You can see all our benefits here -Our Benefits

 

Inclusion, diversity and belonging. 

 

We’re anInclusiveemployer. We celebrate and value different backgrounds and cultures across Royal London. Our diverse people and perspectives give us a range of skills which are recognised and respected – whatever their background. 

 

 

 

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.