Designining, building, and maintaining scalable, well-tested dbt data models that power analytics across commercial, marketing, health outcomes, and clinical operations domains.

Acting  as a senior technical voice within the analytics engineering function, setting standards for data modelling, testing, documentation, and code quality.

Leading end-to-end delivery of analytical projects: scoping requirements, managing dependencies, identifying critical path and risks, and communicating progress to stakeholders.

Mentoring data scientist and analyst data modelling capabilities ; build a culture of best practices, code review, and continuous learning across the data team.

Partnering with clinical, commercial, and product teams to translate complex domain requirements into robust, well-documented data models.

Evaluating, trialing, and championing AI-powered analytics tooling - shaping Numan's strategic direction on emerging technologies and what we adopt as a company.

Driving  data quality, reliability, and governance across the transformation layer, proactively monitoring consumption and resolving issues before they impact stakeholders.

3–5+ years of experience in an analytics engineering, data engineering, or business intelligence role.

Proven track record of owning end-to-end data model design across multiple business domains — ideally spanning at least two of: commercial, marketing, health outcomes, or clinical/operational analytics.

Strong stakeholder management skills, with the confidence to translate complex technical concepts for non-technical audiences including clinical and commercial teams.

Experience setting technical standards and delivering high-quality work within a small, high-performing data team.

Self-motivated and pragmatic, with a strong instinct for high-impact solutions over over-engineering.

Genuine curiosity about AI and emerging analytics technologies, with a desire to help shape how Numan adopts them.

Expert-level SQL: window functions, CTEs, advanced aggregations, and cloud warehouse-specific optimisation.

Deep proficiency in dbt: models, macros, tests, packages, documentation, exposures, and CI/CD integration.

Strong working knowledge of Google BigQuery (or equivalent cloud data warehouse): query optimisation, incremental loading strategies, and cost management.

Experience with Looker (LookML) or an equivalent BI layer, with a solid understanding of consumption-layer design.

Dimensional data modelling expertise: star schemas, slowly changing dimensions, and when to denormalise.

Strong written and verbal communication skills; able to deliver clear, insight-led presentations that tell a compelling story to both technical and non-technical stakeholders.

Understanding of data privacy, governance, and compliance requirements — with particular sensitivity to regulated healthcare data.

Practical experience with AI-assisted analytics workflows, or a strong demonstrated appetite to work at the frontier of AI in analytics.

Worked in a digital consumer scale-up, with experience of delivering high quality work at pace.

Hands-on experience evaluating or implementing AI/LLM tooling in a data or analytics context (e.g. semantic layers, natural language querying, AI-assisted documentation).