About the role
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.