Data Engineer; Analytical Engineering

Location:
Alderley Park (Wilmslow) or Glasgow

Working Style:
Hybrid 50% home/office based

We have a fantastic opportunity for an experienced Data Engineer to join Royal London’s Analytics Engineering Team within our Group Data & AI Office function. As a Data Engineer you will define, manage, and deliver the data, tools, and other technical assets to enable analytics, data science, and machine learning projects. These initiatives will create insights, answer key business questions, solve business problems, and support decision making at all levels of the organisation.

Supporting the Senior Data Engineers in their role as technical lead for the team, helping to set and maintain technical standards. We value great communication skills, as you’ll be interacting with internal business areas as an SME for data, tooling and the technical practice around data engineering, analytics, data science and machine learning.

The purpose of the Analytics Engineering team is to create business value through delivery of specific Analytics, Data Science, Machine Learning and Artificial Intelligence projects and initiatives. These initiatives will create insights, answer key business questions, solve business problems, and support decision making at all levels of the organisation. This team supports the other teams within the Analytics & Insight function, providing them with a Data and Analytics service to support their insight activities.

The team will also act as a Centre-of-Excellence in Analytics and Data Science, helping to take forward the Group’s capability in these areas and our ambition to become “Data Led”.

This role is suited to an experienced Data Engineer who has worked on large enterprise data programmes, with advanced experience of Databricks and data analytics.

More about the role:

• Source and prepare data for use in Business Intelligence (BI), Analytics, Data Science and Generative AI projects and initiatives.
• Source, evaluate, interpret, model, and manage multiple disparate data sources.
• Understand, own, and manage the team’s data – working with business, technology, and external partners.
• Design, develop and manage the deployment of data pipelines in our cloud platforms (Databricks, primarily), and help the manage heritage on-premises (SQL Server primarily) pipelines and the migration of these.
• Prototype solutions to explore business hypothesis in an agile and iterative way which supports a learn fast/fail fast methodology.
• Product ionise data pipelines created by the team through CI/CD so that they are available for consumption for the business, data science, and data visualisation teams, respectively.
• Maintain knowledge of data engineering and machine learning engineering practices including keeping abreast of new developments and changes in the field.
• Maintain knowledge of data-related technologies and practices.
• Help to ensure that robust software engineering practices are adhered to by the team.

More about you:

• Advanced experience of cloud-based Data and Analytics platforms and technologies, Databricks knowledge is essential and some exposure to experience of Fabric and/or Snowflake would be advantageous.
• Advanced experience in data engineering and application of data management design patterns.
• Ideally will have experience in creating Genie Agents, configuring data assets for Natural Language Querying and using Metric Views to create semantic layers.
• Experience of programming languages: SQL, python & py Spark etc.
• Experience of development practices: the use of versioning tools such as Git Hub, work tracking tools such as Azure Dev Ops, or equivalents.
• A proven track record in working in cross-functional projects to a successful conclusion.
• Experience…