Lead Data Architect

Role overview This is a data-heavy, hands-on leadership position for someone who can dig deep into complex datasets personally while guiding a team through equally demanding problems. The work spans deep-dive analyses, statistical modelling, mentoring analysts, and owning the technical roadmap for the analytics function. The expectation is a strong individual contributor who sets the technical bar and unblocks others on genuinely hard work.

Responsibilities - Lead and execute complex, high-stakes analyses including deep-dive investigations, statistical modelling, and multi-source data problems. - Guide and unblock the team on query optimisation, data modelling challenges, messy datasets, and tricky edge cases. - Write advanced, performant SQL against large and complex datasets, including multi-table joins, window functions, and poorly documented schemas. - Build and maintain robust data models and pipelines in partnership with Data Engineering. - Apply statistical methods such as experimentation, A/B testing, regression, cohort, and trend analysis. - Design dashboards and reporting frameworks, and judge when a deeper custom analysis is the better answer. - Set and enforce technical standards for code review, data quality checks, analytical rigour, and documentation. - Translate ambiguous business questions into structured, technically sound analytical approaches and present findings to senior stakeholders.

Requirements - 9+ years of hands-on data analysis experience with demonstrated depth on complex, high-volume datasets. - 2+ years leading or mentoring analysts on technically difficult work. - Expert-level SQL fluency, including window functions, query optimisation, and untangling undocumented schemas. - Experience with cloud data warehouse or lakehouse platforms such as Snowflake and Databricks. - Solid grounding in statistics including experimentation design, hypothesis testing, and regression. - Experience with a BI/visualisation tool such as Tableau, Looker, or Power BI. - Ability to independently solve ambiguous, multi-layered data problems end to end. - Clear communication of complex technical findings to senior non-technical audiences.

Nice to have - Direct experience designing and analysing A/B tests or experimentation frameworks. - Exposure to data engineering concepts such as schema design, ETL, and data warehousing. - Formal people management experience including performance and growth planning.