Analytics Engineer

Requirements

• 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 Click House)
• 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
• (Desirable) 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
• (Desirable) A track record of introducing quality standards or tooling that measurably raised a team’s output, not just your own
• (Desirable) Strong first‑principles stakeholder management: you’d rather ask the awkward scoping question upfront than build the wrong thing twice
• (Desirable) Experience in energy, or another sector with real physical or financial complexity underneath the data
• 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

• 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
• 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