(Senior) Backend Engineer — Trading & Asset Optimization

Job description

(Senior) Backend Engineer — Trading & Asset Optimization Why Now European energy costs run 2× those in the US and China. Electricity demand is on track to double by 2050. Volatility and complexity aren't temporary — they are structural. trawa is building the solution: the first integrated B2B energy platform that bundles procurement, energy management, and flexibility steering into one product. Supply, Manage, Flex — together they unlock up to 30% in recurring electricity cost savings, while shifting companies onto renewable energy. We are the fastest-growing B2B energy platform in Europe — active in Germany and Austria today, expanding to a third market in 2027. The window is open, and we intend to own it. About trawa trawa is based in Berlin with ~100 FTE, ~30 of whom work in Product & Tech. We have raised over €35M to date — including a €24M Series A in May 2025 — backed by Balderton, Headline, Speedinvest, AENU, Magnetic, and Tiny VC. All three founders are on board and fully committed to building the utility of the future. About the role You'll join the team that owns energy trading and asset optimization at trawa - the engine that turns market signals and live asset data into smart, automated decisions. It's a data-intensive, high-stakes part of trawa's tech platform where good engineering directly moves the numbers that matter. We're looking for someone who loves building complex systems, takes real ownership of what they ship, and makes the people around them better. You'll shape architecture, drive initiatives end-to-end, and help define how the team builds. What you'll work on Designing and building the backend systems behind our trading and asset-optimization platform. The services, data flows, and logic that need to be both fast and dependable. Owning initiatives end to end: gathering requirements with product and trading colleagues, shaping the approach, and rolling it out iteratively. Working at the intersection of backend and data - modeling and moving data so it's reliable, explorable, and useful to the people who depend on it, including our data scientists and quantitative traders. Raising the bar on quality, observability, and how we reason about correctness in a system where mistakes are expensive. Operating and maintaining what we build. We stay with our systems after launch: keeping them healthy in production, learning from how they beh…