Staff Data Engineer | Spain | Remote

Talent-R; España; Presencial; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

Talent‑R is building the go‑to platform in commodity trading, empowering trading desks with a scalable data backbone. This role focuses on architecture, reliability and end‑to‑end data infrastructure across real‑time and analytical workloads.

We seek a hands‑on Staff‑level data engineer who owns architecture, not just code, with a track record delivering production data systems. Collaboration with AI initiatives and enterprise clients is central to the role.

Formación

• 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.

Responsabilidades

• Lead pipeline architecture: Design, build and evolve scalable ETL frameworks powering real-time and analytical processing.
• Own platform health: Optimise latency, throughput and reliability as data volumes scale.
• Bridge data and backend: 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.

Conocimientos

Data pipeline architecture

Production-grade data systems

Cross-functional collaboration

High-level architecture

Low-level implementation

Herramientas

Flink

Spark

Kafka

Redis

Postgres

AWS

GCP

Descripción del empleo

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.