Description

About the Role

You'll design and develop modern data infrastructure and applications, enabling portfolio managers and researchers to access high-quality, scalable data solutions. The role involves working across ingestion, ETL, data quality, storage, and APIs, while collaborating closely with quantitative engineers, researchers, and technical teams in a global environment.


Key Responsibilities

• Build and optimize data pipelines and platforms for large-scale, complex datasets.
• Design and implement robust ETL processes and data models for batch and streaming workflows.
• Collaborate with quants and engineers to deliver scalable solutions aligned with business needs.
• Develop APIs and tools to enable efficient data consumption and integration.
• Ensure performance, reliability, and scalability across distributed systems.

Ideal Candidate Profile

• 5+ years of experience in data engineering or similar data-intensive roles.
• Strong Python skills and experience with data libraries (e.g., Pandas, Polars, Dask, PySpark).
• Expertise in SQL and relational databases; familiarity with columnar formats (Parquet/Arrow).
• Experience with distributed systems and messaging tools (e.g., Kafka, Redis).
• Knowledge of data lake/warehouse architectures and performance tuning.
• Excellent problem-solving and communication skills.
• Bonus: Experience with REST APIs, monitoring tools (Prometheus, Grafana), and financial datasets.

If you feel the role is a good fit - apply today!