Descripción de la oferta
About the companyWe're hiring for the go-to platform in commodity trading, the tool trading desks rely on to make pre-trade calls.Closed a $42M Series B in February 2025 and scaling fastAround 130 people across offices in Switzerland, London and Spain, with the Spain hub set to keep growing through 2026Investing heavily in AI, including a new Forward Deployed Engineer function built for enterprise clientsA lean, senior team that moves like an early-stage Stripe or Palantir: high bar, real ownership, minimal red tapeData is the backbone of that ambition. Every AI initiative and every trader-facing insight runs through the pipelines this team owns, which makes this a genuinely architecture-level seat, not just an execution role.About the roleDay-to-day:Lead pipeline architecture: Design, build and evolve scalable ETL frameworks powering real-time and analytical processingOwn platform health: Optimise for latency, throughput and reliability as data volumes scaleBridge data and backend: Work closely with engineering and stakeholders to align infra with product and trader-facing outcomesDrive architecture decisions: Shape pipeline reliability, data quality and scalability across the platformShape target architecture: Define how data gets ingested, transformed, stored and served as the platform growsMentor engineers: Through design reviews, technical discussions and hands-on knowledge sharingWhat you'll need:7+ years as a data or software engineer with production-grade data systems delivered2+ years in a product-focused organisation, working cross-functionallyProven track record scaling data-intensive pipelines in productionHands-on with Flink or Spark (stream and batch processing)Comfortable across Kotlin, Python and TypeScriptEqually sharp on high-level architecture and low-level implementationExperience deploying data infra on AWS or GCPHands-on with Kafka, Redis and/or clustered PostgresWho should apply?A hands-on Staff-level data engineer who wants ownership over architecture, not just code, and who's energised by being handed a problem instead of a spec. Bonus points if you've worked with Apache Iceberg, dbt, or built data infra to support AI agents.
