Lead, coach, and grow a high-performing analytics engineering team
Own the technical direction around data warehouse design, modelling, and modernisation
Ensure best practices in dimensional modelling, data quality, testing, and documentation
Collaborate with embedded analytics teams to build and maintain trusted, scalable data products
Plan, prioritise, and deliver projects in alignment with business goals and timelines
Review and contribute to technical designs, DBT models, SQL logic, and pipeline architectures
Introduce and enable AI-driven analytics and automation across data products where appropriate
Drive Snowflake cost optimisation and ensure efficient resource usage across environments
Improve automation, data lineage, observability, and self-service capabilities
Work closely with Data Engineering and Platform teams on roadmap alignment and resource planning
Foster a culture of continuous improvement, knowledge sharing, and operational excellence

Strong technical background with hands-on experience in data warehouse design and development
5+ years in data engineering, analytics engineering, or equivalent
2+ years leading or managing technical teams
Deep understanding of dimensional modelling and data warehousing principles (star/snowflake schemas, slowly changing dimensions, etc.)
Expert SQL and strong experience with DBT and modern ELT pipelines
Experience with Snowflake and AWS services
Experience managing and delivering complex data or analytics projects
Ability to guide architectural decisions and uphold engineering standards
Strong communication skills and stakeholder management experience.
Nice to Have
Experience with Snowflake and AWS services
Familiarity with data quality/observability tooling and governance practices
Python experience for pipeline development or automation
Exposure to AI/ML–assisted analytics or productivity tooling

Data Manager – Engineering

Key skills