📈 Who We Are:

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

Every price tem quotes and every risk position it holds starts as data, and none of it works if that data can't be trusted. tem is building the AI native infrastructure for how electricity is bought, sold, and priced, and the Data Service plays a critical role in that, end to end: from ingestion through to the semantic layer the rest of the business runs on. Analytics engineering sits right in the middle of it, on dbt, Airflow, and ClickHouse, with Omni as the semantic layer on top.

Analytics engineering at tem is currently a centralised team, and this role is about expanding that remit further into new parts of the business. The domain models you build won't just feed dashboards, they'll power commercial, financial, risk, and operational processes across the company, and increasingly, the AI agents making decisions alongside the humans who use them.

tem is AI native from the ground up, and its agents are only as sharp as the context a human builds into the data beneath them. That human touch, giving AI real context to reason with instead of just raw data, is core to what makes this role matter.

You'll join a small, fast-moving analytics engineering team, reporting to the Analytics Engineering Manager, and your work will reach a lot further than the data team. You'll work directly with engineers, product managers, and salespeople across the business, taking on open-ended problems and turning them into concrete, trusted outputs, because tem's domain layer needs someone who thinks in systems, not tickets.

In your first few months, you'll get under the hood of tem's dbt project and warehouse, and take ownership of extending analytics engineering's reach into a new part of the business. A year in, you'll have shipped process changes that measurably improve how the analytics engineering function ships, and delivered modelling or infrastructure work that a large part of the business now depends on. That reach is only going to grow: tem has big, bold bets on the table, like international expansion and new ways of bringing its technology to other businesses, and the domain layer you build needs to be ready for that.

This is a hands-on, individual contributor role with no direct reports, but real technical ownership: you set the patterns other analytics engineers follow, and you'll have genuine influence over how tem defines its own metrics.

🚀 Responsibilities

• Set and raise the bar on analytics engineering standards. Define the patterns, testing, and review practices that keep dbt models across the business consistent, documented, and trustworthy without you personally checking every one.

• Own the context layer. Bring the semantic layer (Omni) and the underlying domain models together into one place the business, human or AI, can query with confidence.

• Build the domain model from first principles. Take tem's data from raw source to a structured, trusted layer that powers commercial, financial, risk, and operational decisions, not just dashboards.

• Expand analytics engineering's reach across the business. Integrate new data sources, product and platform events, and the tools other departments run on, taking analytics engineering from a centralised function into new corners of the company.

• Help tem think bigger. As the business looks at big bets like international expansion and new ways of bringing its technology to other companies, help build a domain layer that's ready to go with it.

• Partner across the business, not just the data team. Work directly with engineers, product managers, and salespeople to understand what they're actually trying to achieve, then turn that into models that hold up under real use.

🎯 Requirements

Must haves

• You've built or reworked a domain layer before, hit the failure states, and learned what good looks like the hard way.

• Deep, production dbt experience: custom macros, reusable patterns, and real work optimising models that are genuinely expensive to run.

• Excellent SQL and comfort working on a modern data warehouse at real scale (tem runs ClickHouse).

• Hands-on experience with a semantic layer or BI modelling tool (Omni, Looker, or similar), with genuine influence over how metrics get defined, not just how they get built.

• A genuine eye for detail and real QA discipline: you check your own work and care about getting a definition right without needing someone else to catch it, while still keeping pace with a fast-moving business.

Nice to haves

• Experience with commercial data, like sales funnels or CRM pipelines, or with portfolio and financial trading data, including risk, hedging, forecasting, or time-series modelling.

• A track record of introducing quality standards or tooling that measurably raised a team's output, not just your own.

• Strong first-principles stakeholder management: you'd rather ask the awkward scoping question upfront than build the wrong thing twice.

• Experience in energy, or another sector with real physical or financial complexity underneath the data.

🗣️ 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.

1. First call with our Talent Team (30 mins). This is to understand your experience, motivations, and discuss the role in more detail.

2. Behaviour Interview with our Analytics Engineering Manager (75 mins). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working.

3. Technical Interview with the Team (60 mins). You'll meet with potential peers in this session and work through a live technical exercise.

4. Bar Raise 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.

We welcome applications from people of all backgrounds, experiences, and identities, including those that are traditionally underrepresented in the tech and energy sectors. 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.