OPPORTUNITY OVERVIEW

Our client is a leader in quantitative trading, leveraging cutting-edge technology and petabyte-scale data to drive global high frequency trading strategies. We’re seeking an experiencedFront Office Python Developerwith a passion for large-scale data engineering to enhance their proprietary trading platform.
This role sits at the intersection of trading, data infrastructure, and quantitative research. You’ll work directly with traders, quants, and other developers to build scalable data pipelines, integrate structured/unstructured datasets, and optimize high-performance querying systems—all while shaping the future of our real-time trading infrastructure.

MUST-HAVE QUALIFICATIONS

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• StrongPython(production-level, with expertise in data libraries like Pandas, NumPy, PySpark, or Dask).

• Experience withlarge-scale data systems(SQL/NoSQL databases, distributed processing, or cloud data platforms).
• Proven ability toown data pipelines end-to-end—from ingestion to analytics-ready outputs.
• Familiarity withmodern data stack tools(e.g., Airflow, Kafka, Kubernetes, Iceberg/Databricks, or similar).
• Understanding offinancial markets/trading data(tick data, order books, reference data).
• Strong collaboration skills—able to work with quants, traders, and infra teams.

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• Own and optimizelarge-scale data pipelines (petabyte-scale data lakes) feeding into trading strategies.

• Design and implementhigh-performance ETL workflows, integrating diverse data sources (market data, alternative datasets, unstructured feeds).
• Collaborate with quants and tradersto translate research concepts into efficient, scalable data solutions.
• Develop toolingfor real-time data validation, monitoring, and visualization to ensure data integrity.
• Enhance our proprietary data platformusing modern open-source technologies (e.g., distributed compute, cloud-native services, containers, streaming frameworks).
• Solve complex data challenges—low-latency processing, schema design for time-series data, and optimizing queries across massive datasets.