Senior Analytics Engineer, tem

• Strong hands-on experience as an Analytics Engineer in a fast-moving environment
• Deep, practical dbt experience, including:
• Incremental models
• Custom macros
• Debugging and optimising slow or expensive models
• Project structure and maintainability
• Excellent SQL and solid data modelling fundamentals
• Experience with a semantic layer or BI modelling tool (Omni, Looker, or similar)
• Experience translating business questions into metric definitions with stakeholders
• Comfortable working with ambiguity and limited process

Desirable

• We're looking for a Senior Analytics Engineer to help build and shape the analytics foundation of a growing startup
• You'll join a small data team and work on the analytics layer end-to-end: core data models, trusted metrics, and the patterns that enable the rest of the company to use data with confidence
• This is a hands-on, individual contributor role (no people management), with significant technical ownership across the business
• You'll work primarily with dbt for transformations and Omni as our semantic and analytics layer, partnering closely with Marketing, Finance, Operations, and Data Engineering
• In your first few months, you'll get fully up to speed on our warehouse and dbt project and start shipping production-ready models, take ownership of at least one core business area (e.g. Marketing or Revenue metrics) to improve structure, documentation and consistency, and build strong relationships with stakeholders — becoming a trusted point of contact for analytics questions
• Build and maintain the analytics layer: Design, build, and maintain core dbt models that represent the business (e.g. customers, revenue, marketing performance, operations) and keep them production-ready — contributing to the source of truth the business operates on
• Implement and document company metrics: Partner with stakeholders to understand metric needs, translate them into clear definitions, and implement them in Omni so teams can self-serve with confidence
• Deliver cross-functional analytics projects: Work across multiple domains and teams to ship high-impact analytics work — coordinating with stakeholders, managing your own sequencing, and seeing projects through to done
• Make pragmatic modelling trade-offs: Balance speed, accuracy, and long-term maintainability; contribute to patterns that scale as the company grows
• Raise data quality and trust: Apply dbt tests, CI/CD, and documentation standards to catch issues early and reduce regressions
• Partner upstream to fix root causes: Work closely with Data Engineering to diagnose data issues, improve source/warehouse design, and keep the warehouse performant and reliable

• First call with our Talent Team (30 mins) — This is to understand your experience, motivations, and discuss the role in more detail
• Behavioural Interview with our Data Manager (75 mins) — This is your chance to really understand the role, the expectations, and ensure alignment on ways of working
• Technical Interview with the Team (60 mins) — You'll meet with potential peers in this session and work through a live technical exercise
• Culture-Add Interview with Stakeholders (45 mins) — The final session will be with two cross-functional stakeholders, and will explore how your values align with ours. It's designed to be a genuine two-way conversation — your chance to understand what it's really like to work at tem