Purpose of the job
The Lead Data Analytics Engineer is a senior technical leadership role responsible for advancing Enterprise Data Modelling, Analytics Automation and Engineering Empowerment across Global Analytics.
Working directly with the Head of Analytics Engineering, the role translates the Analytics Engineering strategy into technical direction, reusable capabilities and modern engineering practices. The role enables squads to independently deliver trusted, scalable Data Products while maintaining enterprise consistency and engineering excellence.
Key Responsibilities
1. Enterprise Data Modelling
• Lead the enterprise approach to analytical data modelling, including domain models, dimensional models and semantic layers.
• Establish reusable modelling patterns and common business entities that create consistency across Data Products.
• Reduce duplication and improve the performance, scalability and maintainability of analytical models.
• Provide technical leadership for complex and cross-domain modelling challenges.
2. Analytics Automation & AI
• Lead the Analytics Automation agenda, transforming how analytics is developed, tested, deployed, documented and monitored.
• Apply Snowflake Cortex, LLMs, intelligent agents and automation to improve engineering productivity and quality.
• Build reusable automation capabilities and accelerators rather than one-off solutions.
• Identify and industrialise emerging technologies that materially improve Analytics Engineering.
3. Engineering Empowerment
• Create frameworks, tools, templates and reusable components that enable squads to deliver independently and faster.
• Improve developer experience and simplify the journey from development to production.
• Remove recurring technical bottlenecks through self-service and reusable engineering capabilities.
• Enable domain teams to build trusted Data Products within established engineering standards.
4. Technical Leadership & Modernisation
• Act as a senior technical authority for Analytics Engineering, providing direction on complex solutions and technical decisions.
• Drive engineering standards, modernisation, platform performance and reduction of technical debt.
• Mentor engineers and raise technical capability through communities of practice and knowledge sharing.
• Partner with the Head of Analytics Engineering to shape the technical roadmap and future engineering capability.
Key Technology Areas
Snowflake • DBT • SQL • Python • Git/CI/CD • Semantic Layers • Data Contracts • Data Quality & Observability • Snowflake Cortex • LLMs & AI Agents • AWS
Skills & Experience
Must Have
• Strong hands-on Analytics Engineering/Data Engineering experience with deep expertise in data modelling, Snowflake, dbt and SQL.
• Proven experience building automation, reusable engineering frameworks and scalable analytical architectures.
• Strong technical leadership experience, including influencing multiple teams, solving complex engineering challenges and mentoring senior engineers.
• Experience applying GenAI, LLMs, AI agents or Snowflake Cortex to engineering automation.
• A hands-on, outcome-driven engineering leader who leads from the front by designing, building, and delivering solutions. This role requires an individual contributor mindset, someone who is equally comfortable defining strategy and implementing it through working code, prototypes, and production-ready solutions.
Nice to Have
• Python and modern data observability/lineage experience.
• Experience enabling self-service engineering across distributed analytics teams.
Success Measures
• Data Modelling: Greater reuse and consistency of enterprise models with reduced duplication.
• Automation: Measurable reduction in manual engineering effort and improved delivery velocity.
• Empowerment: Squads increasingly able to independently build and operate trusted Data Products.
• Engineering Excellence: Improved reliability, performance, cost efficiency and overall engineering maturity.
• Technical Leadership: Recognised as the technical lead who drives delivery through hands-on contribution, accelerates engineering outcomes, and enables teams by building reusable capabilities rather than relying solely on governance or oversight.
• Innovation: Successful delivery and adoption of AI-powered engineering capabilities, automation frameworks, and modern engineering practices that create measurable business value.
