Python Engineer; Terminal​/Modelling​/Data)

London

The energy transition is the biggest infrastructure buildout in human history. Modo Energy is the data platform at the centre of it.

We build the benchmarking, forecasting, and valuation tools that the world's most serious energy investors, developers, and operators depend on to make decisions. If a battery gets financed, built, or traded anywhere in the world, there's a good chance Modo data was in the room.

Founded in 2019, we're 80+ people across London, New York, Sydney, and Madrid; $30M Series B, AI-native, and moving fast.

This is a rare chance to join a category-defining company at the moment it's scaling globally.

Modo Energy's engineering team builds the systems behind the Modo Terminal, the data, research, and agentic platform that energy market professionals rely on every day. We're growing fast, and our ambitions are growing faster: from serving data and research to letting users (and their AI agents) query, forecast, and act on it directly.

We're hiring backend Python engineers to join one of our product teams:
Terminal, Data, Ko (our AI Analyst), or Modelling. Each team owns a business capability end-to-end, including its own APIs, data pipelines, AWS infrastructure, and developer tooling. Wherever you land, you'll be building production systems from day one, with ownership over what you ship and how.

We're looking for engineers who learn fast and go wide. You won't be boxed into a single layer of the stack; the best people on our teams move fluidly between building a feature, hardening a pipeline, fixing an infra issue, and automating away the next one. If you're the kind of engineer who picks up a new tool, library, or part of the codebase without needing much of a runway, this is built for you.

• Build and maintain backend services and APIs, on Django, FastAPI, or similar, that power a live product used daily by energy market professionals.
• Design and operate data pipelines that ingest, transform, and serve energy market data reliably and at scale.
• Own AWS infrastructure and deployment end-to-end using Terraform, from local development through to production.
• Build the monitoring, observability, and alerting that let your team catch problems before customers do.
• Split your time between shipping new features and keeping the platform healthy. Maintenance, performance, and production issues are a normal, expected part of the job, not an interruption to it.
• Design internal tools such as CLIs, APIs, and MCP servers that make your team and others faster, and that let both humans and AI agents work with our data safely and effectively.
• Work directly with the people who understand the domain (energy analysts, data scientists, modellers) to make sure what you build is accurate and actually useful.
• Contribute to the infrastructure behind our LLM-powered AI Analyst as needed: retrieval pipelines, tool-calling, and the plumbing that lets models work with real energy market data.
• Cross into the parts of the stack outside your core comfort zone when the team needs it, whether that's a bit of frontend or a bit of deeper infra, and pick up what you don't already know.

The Essentials

• 3+ years of professional Python experience, with strong backend fundamentals. You write clean, well-structured code and are comfortable owning something end-to-end, from design through deployment and monitoring.
• Real, hands‑on experience with AWS and infrastructure-as-code (Terraform). You've run production infrastructure, not just experimented with it.
• Proficiency with Django, FastAPI, or a comparable async Python framework.
• Experience with data pipelines and relational databases (PostgreSQL), and building systems that serve data reliably at scale.
• Expert‑level use of AI coding tools (Cursor, Claude Code, Git Hub Copilot, or similar). You know when to trust them, when to intervene, and how to get real leverage from them. We write a lot of code this way.
• A fast learner with good judgement, comfortable making calls on system design and what's good enough to ship, and genuinely energized by picking up unfamiliar tools, stacks, or problem areas rather than staying in one lane.
• Comfortable…