Senior Analytics Engineer
The role
As Numan enters an exciting new chapter of AI-powered analytics, we're looking for a Senior Analytics Engineer to sit at the core of our data platform and drive better decisions across the entire business.
This is one of the most varied analytics engineering roles in digital health. Your work will span multiple domains - from commercial performance and marketing effectiveness through to health outcomes analytics and clinical operations - giving you a genuinely broad view of the business and the full patient journey.
We are embarking on a strategic investment in AI-powered analytics, with a full remit to explore and adopt the latest and greatest technologies. You'll be shaping which tools, approaches, and practices Numan adopts as a company.
Reporting to the Head of Data Engineering and collaborating closely with data scientists, analysts, and stakeholders across the business.
You'll be:
Designing, 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.
We're looking for someone who has:
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.
It's a bonus if you have:
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).
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Skills
- Data
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