Analytics Data Engineer for AI & Big Data Pipelines (Hybrid)

Royal London; Alderley Edge; Hybrid; GBP 60,000 - 95,000; Full time

Benefits offered by this job

28 days holiday

14% employer pension match

Private medical insurance

Job summary

Royal London is seeking an experienced Data Engineer to join the Analytics Engineering Team within the Group Data & AI Office. You will define, manage, and deliver data assets to enable analytics, data science and ML projects, working in a hybrid setup across Alderley Edge/Glasgow.

You will deploy data pipelines on Databricks, integrate SQL Server on-premises heritage, and collaborate with cross-functional teams to set technical standards and practice in data engineering, analytics and ML.

Qualifications

• Extensive experience delivering enterprise data programs.
• Advanced knowledge of Databricks and data analytics.
• Strong stakeholder management and cross-functional collaboration.

Responsibilities

• Source and prepare data for BI, Analytics, Data Science and Generative AI projects.
• Source, evaluate, interpret, model, and manage multiple disparate data sources.
• Understand, own, and manage the team’s data with stakeholders.
• Design, develop and manage deployment of data pipelines on Databricks; migrate on-premise SQL Server pipelines.
• Prototype solutions to test hypotheses in agile iterations.
• Productionise data pipelines via CI/CD for consumption by business, data science and visualization teams.
• Maintain knowledge of data engineering and ML practices and new developments.

Skills

Databricks

SQL

Python

pySpark

GitHub

Azure DevOps

PowerBI

Tableau

R

Tools

Databricks

SQL Server

PowerBI

Tableau

Job description

Royal London is seeking an experienced Data Engineer to join the Analytics Engineering Team within the Group Data & AI Office. You will define, manage, and deliver data assets to enable analytics, data science and ML projects, working in a hybrid setup across Alderley Edge/Glasgow.

You will deploy data pipelines on Databricks, integrate SQL Server on-premises heritage, and collaborate with cross-functional teams to set technical standards and practice in data engineering, analytics and ML.