Staff Data Engineer | Spain | Remote
Summary
A staff-level data engineer designs and owns the ETL/streaming pipeline architecture for a commodity trading platform, optimizing latency, throughput and reliability while mentoring other engineers. Core stack: Flink or Spark, Kafka, Redis, clustered Postgres, Kotlin/Python/TypeScript on AWS or GCP.
We're hiring for the go-to platform in commodity trading, the tool trading desks rely on to make pre-trade calls.
- $42M Series B in February 2025 and scaling fast
- Around 130 people across offices in Switzerland, London and Spain, with the Spain hub set to keep growing through 2026
- Investing heavily in AI, including a new Forward Deployed Engineer function built for enterprise clients
- A lean, senior team that moves like an early-stage Stripe or Palantir: high bar, real ownership, minimal red tape
Data 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 role
Day-to-day:
- Lead pipeline architecture: Design, build and evolve scalable ETL frameworks powering real-time and analytical processing
- Own platform health: Optimise for latency, throughput and reliability as data volumes scale
- Bridge data and backend: Work closely with engineering and stakeholders to align infra with product and trader-facing outcomes
- Drive architecture decisions: Shape pipeline reliability, data quality and scalability across the platform
- Shape target architecture: Define how data gets ingested, transformed, stored and served as the platform grows
- Mentor engineers: Through design reviews, technical discussions and hands‑on knowledge sharing
What you'll need:
- 7+ years as a data or software engineer with production‑grade data systems delivered
- 2+ years in a product‑focused organisation, working cross‑functionally
- Proven track record scaling data‑intensive pipelines in production
- Hands‑on with Flink or Spark (stream and batch processing)
- Comfortable across Kotlin, Python and TypeScript
- Equally sharp on high‑level architecture and low‑level implementation
- Experience deploying data infra on AWS or GCP
- Hands‑on with Kafka, Redis and/or clustered Postgres
Who 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.