Live opening · Posted 2 days ago
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Description supplied by the original job listing.
Responsibilities:
Design and maintain scalable ETL pipelines for real-time and historical market data.
Build reliable, fault-tolerant data ingestion and processing systems.
Ensure data quality through validation, reconciliation, and monitoring frameworks.
Optimize data storage and retrieval for research, backtesting, and live trading.
Manage market data vendor integrations (Bloomberg, Refinitiv, FactSet, exchanges, etc. ).
Collaborate with quant researchers and engineers to build feature-ready datasets.
Support live data feeds, APIs, and production monitoring.
Lead and mentor a small team while driving engineering best practices and technical excellence.
Requirements:
5-8 years of experience in data engineering, market data engineering, or financial data operations.
Strong expertise in Python, SQL, ETL frameworks, and Kafka (Pulsar is a plus).
Solid understanding of tick data, corporate actions, exchange market structure, and vendor feeds.
Experience building scalable, high-performance data pipelines and troubleshooting production systems.
Prior experience in quantitative trading, HFT, proprietary trading, or financial markets.
Familiarity with low-latency market data, market microstructure, and order book data.
Exposure to ML data pipelines, feature engineering, and data preparation.
Nice to Have:
Experience with distributed data processing and cloud platforms.
Knowledge of real-time streaming architectures and event-driven systems.
Background in quantitative research platforms or large-scale financial data infrastructure.
Experience
5-9 yrs
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