Data Production Engineer

at  Hudson River Trading

Hudson River Trading (HRT) is looking for a Data Production Engineer to join our Data team. Data is at the core of everything we do at HRT; we excel at deriving deep insights from all types of data, allowing us to achieve consistent success in a dynamic market.
This role is an opportunity to work directly with live trading teams to support one of the largest automated trading systems in the world. You will write automation, explore data, work closely with our research and trading teams, and interact with a variety of external partners such as data providers, brokers, and exchanges. In addition to being a critical part of the trading process, you will have the opportunity to acquire, analyze, and prepare data for quantitative research.

QUALIFICATIONS

• 2+ years of experience in a data engineering/science role OR a degree in data science or a similar discipline
• Experience in Python strongly preferred
• Experience managing ETL pipelines is a plus
• Experience with financial datasets (e.g. Refinitiv, S&P, Bloomberg) is a big plus
• Comfortable with the Linux command line
• Experienced in at least one SQL dialect (PostgreSQL, MSSQL, MYSQL) and able to use others as needed
• Able to provide technical support in a production trading environment

• Data Engineering: Write tools to classify, onboard, and reconcile data. Onboard datasets, explore data, and automate tasks using a modern Python data stack
• Data Analysis: Parse, analyze, and understand data sets. Perform data reconciliations, validations, and quality checks. Identify and develop new processes within the data request process to enrich data. Assist our researchers in cleaning and featurizing data
• Data Debugging: Find anomalies in derived datasets and trace the issues back to their source. This can include using a mix of deductive reasoning, technical analysis, and communicating with multiple stakeholders in a data pipeline
• Production Support: Provide proactive oversight of our data pipeline, handle inquiries from internal customers, and resolve issues under efficient turnaround times