📈 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.
We’re looking for a Staff Data Engineer to lead the design and evolution of our data platform. This is a high-impact, hands-on role combining technical leadership, system architecture, and product thinking.
You’ll work closely with engineering, data science, and energy domain experts to ensure that data is reliable, scalable, and directly drives business value.
You'll work across the data management service team alongside data and analytics engineers, and in close partnership with energy domain experts, data scientists, and the broader engineering organisation. This is a hands-on senior technical leadership role — you'll be reviewing pull requests and setting architectural direction in the same week.
What makes this genuinely different: you're not inheriting someone else's vision of what a data platform should be. The cultural norms, the standards, the practices — these are yours to define. If you've wanted to build the right thing from the ground up, this is that opportunity.
Technical Leadership
Shape the technical direction across batch and streaming pipelines, setting the architecture others build to
Set standards for pipeline design and data quality
Lead design reviews and mentor other data engineers
Evaluate and introduce tooling where it raises the bar — and make the case for when it doesn't
Data Platform & Pipelines
Build and maintain robust ETL/ELT pipelines
Build systems optimised for high-ingestion, low-latency querying of time-series data (TSDS)
Optimise pipelines for performance, cost, and reliability
Enable self-serve analytics and decision-making across the company
Reliability and observability
Implement data quality frameworks with real teeth: SLAs, automated testing, lineage, and monitoring
Establish practices specific to energy data: late arrivals, reprocessing, backfills, and the failure modes that matter in this domain
Build the observability layer that makes the platform trustworthy without constant human oversight
Scale and performance
Identify and fix the bottlenecks that constrain us today
Optimise pipelines for performance, cost, and reliability as data volumes grow
Architect for the next order of magnitude, not just the next quarter
Technical leadership and culture
Set engineering standards for pipeline design, data quality, and system observability
Lead design reviews and mentor data engineers, raising the bar for how the team works
Act as a multiplier: the people around you should get better because of how you approach problems
Proven experience operating at staff level (ownership of systems, not just pipelines)
Experience building and scaling modern data platforms
A track record of operating at staff or principal level: you've owned systems, shaped technical direction across teams, and influenced how engineering gets done — not just delivered pipelines.
Deep experience building and scaling production data platforms, including high-ingestion time-series workloads, and strong hands-on ability in Python and modern data stack components (orchestration, warehousing, observability).
The ability to design for reliability and scale — you understand the trade-offs in data system design and have made consequential architecture decisions you can speak to clearly.
A product mindset: you care about whether the data is actually useful and used, not just whether the pipeline ran green.
Experience with cloud data infrastructure (AWS or GCP) and a point of view on what good looks like.
The communication skills to lead without authority — influencing technical direction across teams and making the case for the right thing even when it's harder.
Strong programming skills in Python, with experience building production-grade data systems
Experience with modern data stack components (e.g.):
Orchestration: Airflow / Dagster
Warehousing: Snowflake / BigQuery / Redshift / ClickHouse
Streaming (nice to have): Kafka / Flink
Experience with cloud platforms (AWS / GCP)
Experience with data observability and testing practices
Experience in energy or climate tech
Familiarity with time-series data at scale
Experience supporting ML pipelines in production
Background in high-growth startups or scale-ups
The data platform handles tem's current scale without firefighting, and is architected for the next phase of growth
Other teams can access, trust, and use data without routing requests through the data engineering team
There is a tight, reliable feedback loop between data ingestion and consumption: trading, forecasting, and analytics teams make faster decisions because the data is there when they need it
The data engineering team has clearer standards, better practices, and higher output than when you arrived
Competitive salary - our current band for this role is £105,000 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
tem