As an Analytics Engineer, you'll bridge the gap between business requirements and technical delivery, building the curated layer of our Lakehouse platform. You'll transform complex business needs into trusted datasets, dimensional models and scalable data pipelines that empower analytics, reporting and data-driven decision making across the firm.

The role requires strong technical depth, excellent analytical capabilities, and the ability to communicate clearly with stakeholders. It contributes to architectural decisions, modelling standards, documentation, and overall data reliability across the organisation.

What you’ll be doing:

• Design dimensional data models, including facts, dimensions and KPI-driven datasets.
• Build and maintain scalable SQL and PySpark transformation pipelines within Databricks.
• Translate business requirements and source-to-target mappings into robust technical solutions.
• Implement data quality controls, automated testing and monitoring to ensure trusted data outputs.
• Optimise performance and cost across data pipelines and datasets.
• Create and maintain clear documentation, including data models, business rules, lineage and operational processes.
• Collaborate with Data Architects, Data Engineers and the broader Data Analytics to ensure consistency, traceability and alignment to business needs.
• Support the development of semantic models and curated datasets for  analytics teams.
• Contribute to CI/CD practices, code reviews and deployment processes.
• Support data governance, security and compliance requirements.
• Mentor colleagues on data modelling, SQL, Spark and engineering best practice.
• Contribute to the continuous improvement of standards, tooling and ways of working. 

Essential Skills & Experience

• Experience in dimensional and semantic data modelling.
• Knowledge of Power BI and DAX code
• Proven experience designing and delivering solutions within a Databricks Lakehouse environment.
• Advanced SQL skills and strong PySpark/Spark development experience.
• Experience building data pipelines, transformations and curated analytics datasets.
• Knowledge of Delta Lake, Databricks SQL Warehouse, Unity Catalog and Databricks Workflows.
• Experience implementing data quality frameworks and automated testing.
• Understanding of CI/CD practices for data engineering, including Git-based development and deployment pipelines.
• Strong performance optimisation and cost management experience.
• Experience working closely with Data Architects, Data Engineers, Data Analysts and business stakeholders.
• Excellent communication skills, with the ability to explain technical concepts to both technical and non-technical audiences.
• Experience supporting semantic layers and BI consumption.
• Familiarity with Azure technologies such as ADLS, Entra ID, Key Vault and Data Factory, or equivalent cloud services.

Desirable

• Experience with Delta Live Tables (DLT).
• Knowledge of data observability, SLAs/SLOs and lineage-driven impact analysis.
• Experience working in regulated or audited environments.
• Experience contributing to engineering standards, reusable frameworks and developer best practices.
• Previous experience within a professional services environment