• 7+ years of experience across analytics engineering, business intelligence, data products, analytics strategy, or related data roles, with experience leading cross-functional work and influencing senior stakeholders
• Production-grade dbt experience (dbt Core or dbt Cloud) — you have designed and maintained multi-layer models at scale, written macros, implemented tests, and established reusable modelling patterns
• Expert SQL skills — complex analytical queries, window functions, CTEs, aggregations, dimensional modelling, metric logic, and query performance awareness
• Strong Python proficiency for analytics and data engineering use cases — data wrangling, automation, testing, custom tooling, exploratory analysis, or production workflows
• Demonstrated ability to build governed, performant, and user-friendly Tableau dashboards or equivalent BI products from scratch and explain design decisions to executives and business users
• Experience with a modern cloud data warehouse, preferably BigQuery, including materialisation, partitioning, clustering, cost, and performance trade-offs
• Strong understanding of data modelling and analytics architecture — conformed entities, marts, star schema, slowly changing dimensions, semantic layers, metric normalisation, and lineage
• Experience with Git, branch-based development, code review, and CI/CD in a data context — you apply software engineering discipline to analytics code
• Proven ability to lead senior-stakeholder discovery and requirements conversations, communicate technical trade-offs clearly, and convert business needs into an actionable data-product brief
• Experience setting priorities across multiple domains and delivery teams, resolving conflicting requirements, and building alignment without relying solely on formal authority
• Practical experience using AI tools such as Claude, Gemini, GitHub Copilot, or similar to accelerate analysis, coding, documentation, and exploration. You apply sound engineering judgement to generated output: understanding and reviewing the code, validating results against requirements and source data, adding appropriate tests, and considering security, privacy, performance, and maintainability before anything reaches production