Staff Analytics Engineer

About

We are rebuilding the energy transaction, making it transparent and fair.

Our goal is to put power back where it belongs, in the hands of customers and to take on one of the most critical problems of our century, access to low cost electricity.

tem exists to fix a broken global energy market that’s long favoured legacy operators, intermediaries, and opaque pricing. Today’s electricity system was not designed for rapid decarbonisation, AI‑driven efficiency or fair access for the actual users – businesses and generators.

We’ve built the first AI native transaction infrastructure to reinvent how electricity is bought, sold and priced. Our technology is designed to cut out the inefficient fees, automate complex market flows, and bring transparency and fairness to energy transactions at scale.

In late 2025, after extraordinary growth, we closed a $75 million Series B – led by Lightspeed Venture Partners with participation from Albion, Atomico, Allianz, Hitachi Ventures,, Hitachi Ventures, Schroders Capital and others – positioning us for global expansion, deeper product innovation and category leadership.

We’re scaling internationally and building toward a future where AI‑driven infrastructure is foundational to electricity markets worldwide.

Since launch, our modern utility product, known as RED, has already facilitated thousands of business customers and billions in energy transaction value, proving that modern software and AI can transform an industry built on legacy systems.

At tem, we’re not just building another energy company, we’re rearchitecting market infrastructure so that transparency, efficiency and sustainability become the default, not the exception.

The Role

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 ~1 B rows daily)
  • Custom macros
  • Debugging and optimising slow/expensive models
  • dbt project architecture and maintainability
• Excellent