Staff Analytics Engineer, tem

• Strong experience as an Analytics Engineer in a fast-moving environment
• Ability to set direction for analytics engineering (patterns, standards, strategy) and execute hands-on
• Deep, hands-on dbt production experience, including:
• Incremental models at scale (we ingest ~1B rows daily)
• Custom macros
• Debugging and optimising slow/expensive models
• Dbt project architecture and maintainability
• Excellent SQL and strong data modelling fundamentals
• Experience with a semantic layer / BI modelling tool (Omni, Looker, or similar)
• Proven experience defining metrics with business stakeholders
• Comfort operating with ambiguity and limited process
• If you’re excited about this role but not sure you meet every requirement, we’d still love to hear from you. Your unique perspective could be exactly what we’re looking for

Desirable

• We’re looking for a Staff 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 and influence
• 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 design decisions
• Advance the analytics layer end-to-end: Design, build, and maintain core dbt models that represent the business (e.g. customers, revenue, marketing performance, operations) and keep them production-ready. The means creating the source of truth for the business to operate on
• Define and evolve company metrics: Partner with stakeholders to create clear, consistent metric definitions, and implement them in Omni so teams can self-serve with confidence
• Lead cross-domain initiatives: Deliver high-impact analytics engineering projects that span multiple domains and teams—driving alignment, sequencing work, and shipping outcomes
• Make pragmatic modelling trade-offs: Balance speed, accuracy, and long-term maintainability; set patterns that scale as the company grows
• Raise data quality and trust: Introduce and maintain standards using dbt tests, CI/CD, documentation, and lightweight governance; 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

• Our processes normally take around 2-3 weeks from first call to offer - please let us know about any adjustments to timelines that may be required
• First call with our Talent Team (30 Mins). This is to understand your experience, motivations, and discuss the role in more detail
• Behaviour Interview with our Data Manager (60 Mins). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working
• Technical Interview with the Team (90 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, and is designed to be a genuine two-way conversation, your chance to understand what it’s really like to work at tem