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.

🚀 Responsibilities

• 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.

🎯 Requirements

Must-haves

• 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.

Nice-to-haves

• Experience working closely with Marketing and Finance data.

• Exposure to early-stage analytics stacks or building analytics “from scratch”.

• Familiarity with experimentation, funnel analysis, or unit economics.

✨ Benefits & Perks:

• Competitive salary - our current band for this role is £100,500 or equivalent in local currency.

• We review salaries twice a year using real-time market data, with transparent, consistent pay for the same role and level.

• Stock Options - everyone on the team has ownership in our mission.

• 25 days holiday + public holidays - Swap public holidays for ones that matter most to you. Plus, get an extra day off for your birthday 🎉.

• Remote & flexible working - We’re fully remote, distributed across Europe with clear core hours, and no internal meetings on Friday afternoons.

Home working & wellbeing budgets:

• Up to £1,200 / €1,200 annually to upgrade your remote setup (co-working passes, equipment, etc.).

• Up to £150 / €150 monthly on anything that supports your wellbeing - from therapy to gym memberships to meditation apps.

🗣️ Interview Process:

• 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.