Algorithmic Trading Data Engineer

We are looking for a Trading Data Engineer to take full ownership of our data architecture, specifically designing and managing our TimescaleDB and MinIO environments. Your mission will be to build the reusable pipelines and optimized datasets that serve as the technical foundation for our algorithmic trading and analytical teams. We need a proactive professional who can bridge the gap between raw market feeds and trading strategy while helping us scale our data processes from the ground up. Our team is scaling up and looking for support to enable progress.

What will you do?

• Architect Storage Solutions: You will take full ownership of our data layer, designing and managing storage structures in TimescaleDB and MinIO. This includes deciding on table schemas, partitioning strategies, and optimizing Parquet file sizing.
• Build Production Pipelines: Develop and maintain reusable ETL pipelines that ingest, clean, and preprocess raw market for our quantitative analysts.
• Ensure Data Integrity: Implement automated checks to validate data quality, ensuring that the "fuel" for our trading algorithms is accurate and reliable.
• Shape the Infrastructure: As a key member of a scaling team, you will help define repository structures and coding standards for our data environment.
• Technical Synchronization: Collaborate with other data teams within the Trading department to align ETL processes and ensure a unified approach to data architecture.
• Support Algo-Trading: Act as the technical bridge between raw data sources and the analytical team, translating complex trading requirements into high-performance data loaders.

Skills you need to have:

• Data Engineering Core: Strong proficiency in Python and SQL. You should be comfortable building production-grade, reusable pipelines.
• Database Expertise: Hands-on experience with TimescaleDB (or advanced PostgreSQL) and object storage solutions like MinIO/S3.
• Infrastructure Knowledge: Familiarity with OpenShift (or Kubernetes) and an understanding of how to deploy and scale services in a containerized environment.
• Optimization Mindset: You know how to structure data for performance—balancing write-speed for incoming market feeds with read-speed for backtesting.
• Analytical Background: Experience with data preprocessing and an understanding of time-series data. Knowledge of energy markets or commodity trading is a significant advantage.
• Proactive Problem Solving: We are looking for someone who doesn't just "complete tasks" but actively identifies bottlenecks in the data flow and proposes innovative ways to fix them.
• Communication: Ability to work closely with Algo Traders and Analysts, explaining technical trade-offs in a clear, no-nonsense manner.

In case of interest in this position, please send us your CV and cover letter by means of the "APPLY" button.