Centiva Capital
Please send resumes to Tech-Recruiting@centivacapital.com.
Key Responsibilities Develop and maintain scalable Python-based ETL pipelines for ingesting and transforming market data from multiple sources. Build and manage cloud data lake solutions (AWS/Databricks) for storing and retrieving large volumes of structured and unstructured data. Implement rigorous data quality, validation, and cleansing routines to ensure the accuracy of financial time-series data. Optimize data workflows for low latency and high throughput, critical for quantitative research, trading strategies, and risk management. Collaborate with portfolio managers, quantitative researchers, and traders to tailor data solutions that support modeling, strategy development, and trading insights. Contribute to the firms security master database design and implementation. Analyze and extract insights from datasets to inform trading and risk management decisions. Document system architecture, data flow processes, and technical solutions to ensure transparency and reproducibility.
Requirements Bachelors (or higher) degree in Computer Science, Engineering, Mathematics, Statistics, or a quantitative discipline. At least 5 years of relevant experience in developing python-based financial software. Demonstrated expertise in Python, including data manipulation experience with Pandas. Some exposure to financial datasets across various asset classes. Experience collaborating with quantitative analysts to support the quantitative research and modeling process. Proficiency in working within a Linux environment. Strong foundation in mathematics and statistics. Ability to thrive in a fast-paced, detail-oriented environment under pressure. Excellent problem-solving skills along with strong verbal and written communication abilities. NYC or London based with some in-office presence required.
Preferred Experience with Kafka or other streaming technologies. Understanding of financial market data, symbology, and reference data across equities, futures, credit, indices, and OTC asset classes. Prior work in a hedgefund, proptrading, or other quantitativefinance environment. Experience with cloud platforms such as Azure / AWS. Exposure to LLM/AI solution architecture and integrating AI/ML models into data pipelines.
Key Responsibilities Develop and maintain scalable Python-based ETL pipelines for ingesting and transforming market data from multiple sources. Build and manage cloud data lake solutions (AWS/Databricks) for storing and retrieving large volumes of structured and unstructured data. Implement rigorous data quality, validation, and cleansing routines to ensure the accuracy of financial time-series data. Optimize data workflows for low latency and high throughput, critical for quantitative research, trading strategies, and risk management. Collaborate with portfolio managers, quantitative researchers, and traders to tailor data solutions that support modeling, strategy development, and trading insights. Contribute to the firms security master database design and implementation. Analyze and extract insights from datasets to inform trading and risk management decisions. Document system architecture, data flow processes, and technical solutions to ensure transparency and reproducibility.
Requirements Bachelors (or higher) degree in Computer Science, Engineering, Mathematics, Statistics, or a quantitative discipline. At least 5 years of relevant experience in developing python-based financial software. Demonstrated expertise in Python, including data manipulation experience with Pandas. Some exposure to financial datasets across various asset classes. Experience collaborating with quantitative analysts to support the quantitative research and modeling process. Proficiency in working within a Linux environment. Strong foundation in mathematics and statistics. Ability to thrive in a fast-paced, detail-oriented environment under pressure. Excellent problem-solving skills along with strong verbal and written communication abilities. NYC or London based with some in-office presence required.
Preferred Experience with Kafka or other streaming technologies. Understanding of financial market data, symbology, and reference data across equities, futures, credit, indices, and OTC asset classes. Prior work in a hedgefund, proptrading, or other quantitativefinance environment. Experience with cloud platforms such as Azure / AWS. Exposure to LLM/AI solution architecture and integrating AI/ML models into data pipelines.