Design & Build Models: Design, build, and maintain clean, well‑structured data models that support analytics, reporting, and self‑serve use cases.
Write Efficient SQL: Write efficient, scalable SQL to transform raw data into trusted, business‑ready datasets, keeping reusability front of mind.
Embed AI & LLMs: Help shape how we use AI within our analytics workflows. Think critically about how data structure and metadata can support LLMs in generating accurate, meaningful insights.
Ensure Data Quality: Contribute to improving data quality, testing, and documentation across the analytics layer.
Collaborate with Stakeholders: Work closely with stakeholders and users to understand key questions and ensure data is modelled to answer them effectively.
