Principal Data Engineer

Aegon

Edinburgh

5 months ago

Applications closed

Related Jobs

View all jobs

Data Analyst - Aerospace

Data Analyst - Sc cleared

Principal, AI Data Science

Social network you want to login/join with:

Location: Edinburgh or Witham (We believe in the power of in-person collaboration, and our hybrid model requires colleagues to be in the office a minimum of 40% of their time)

Salary: A competitive salary from £63,440 - £95,160,depending on the experience you can bring

Closing date: 21st July 2025

We’re a company of ambitious, collaborative problem-solvers who get things done – we’re looking for like-minded people to join us.

We help people live their best lives. We help them with the big stuff, for the moments that matter: Pensions, Savings, Investments. At Aegon, we strive in creating a diverse organisation that plays a meaningful role in driving greater equity, inclusion and belonging.

Data is crucial to our organisation — it shapes how we operate every day and is fundamental to achieving our goals. As we continue to evolve, our data function is undergoing a transformation. We're reimagining how we work, incrementally improving our data architecture, strengthening our engineering practices, expanding our capabilities, and driving innovation across the business.

We are hiring for multiple Principal Data Engineers in Edinburgh or Witham. In this role, you will lead the design and development of complex cloud-first data solutions in AWS, ensuring high-quality, scalable code and engineering excellence. You will collaborate with data engineers, product managers, analysts, BI engineers, and data users to deliver innovative, self-service data solutions. You’ll also work with senior stakeholders to shape the roadmap and backlog for the data capability team, aligning with strategic business objectives. Your responsibilities will span the entire data lifecycle, from design to production — ensuring solutions meet defined standards.

Key Responsibilities:

Team Leadership:Provide technical guidance, foster continuous learning, and take ownership of team capabilities and deliverables.
Architect and Build Scalable Data Solutions:Design and develop scalable data solutions using AWS-native technologies (DMS, Glue, RDS, Lambda, Python, PySpark, Athena, DynamoDB, PostgreSQL, Kinesis, SQS, SNS) and BI tools (Tableau, Power BI, QuickSight).
Cross-Functional Collaboration:Working closely with cross-functional teams to gather business requirements, designing effective data solutions, and ensuring timely and high-quality project delivery.
Champion Engineering Best Practices:Establish coding standards, conduct code reviews, and promote quality assurance practices.
Ensuring Data Quality and Reliability:Develop and maintain automated testing frameworks for data pipelines.
Platform Design:Architect data platform solutions aligned with AUK Architecture team standards.

We’d love to hear from you if you have:

Experience in a Senior, Lead, or Principal Data Engineer role.
Strong track record of delivering cloud-based data platforms, data lakes, BI, and advanced analytics solutions.
Deep understanding of data architecture principles and data modelling techniques in modern serverless cloud environments.
Hands-on experience with Python, PySpark, SQL, and infrastructure-as-code tools (AWS CloudFormation, Terraform, AWS CLI).
Extensive expertise in AWS services and architecture, including EC2, S3, DMS, Lambda, API Gateway, AWS Glue, and QuickSight or similar in other Cloud environments like GCP / Azure.
Proficiency in building scalable, serverless data pipelines using cloud-native ETL tools, such as AWS Glue, Azure Data Factory, or Google Dataflow.
Experience with SQL and NoSQL cloud databases.
Strong grasp of data engineering principles and best practices.
Ability to implement robust data quality controls and monitoring frameworks.
Knowledge of data security best practices.
Proven ability to lead and mentor engineering teams.
Skilled in translating business requirements into technical specifications.

What’s in it for you?

A non-contributory pension between 8%-12%
A discretionary bonus, depending on personal and company performance
36 days leave per year (including bank holidays, pro-rated for part-time)

We also offer private medical cover, life assurance, critical illness cover, enhanced parental leave and a variety of lifestyle benefits to help our employees live their best lives, including retail discount vouchers, cycle2work scheme, subsidised restaurant and online GP appointments. To find out more about what to expect at Aegon .

We're looking for talented individuals who are ready to make a real impact. If you're excited about new challenges and want to work with a team that values your skills, apply today!

The legal bits

We’ll need you to confirm you have the right to work in the UK. If we offer you a job and you accept, there are some checks we need to complete before you can start with us. This will include a credit and criminal record check, as well as providing satisfactory references.

Equal Opportunity Employer:

We are an equal opportunities employer and welcome applications from all suitably qualified persons regardless of their age, disability, race, religion/belief, gender, sexualorientationor gender identity.

#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Apr 9, 2026

Products

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Mar 24, 2026

Jobs

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

Feb 11, 2026

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.