Software Engineer—Data

Vola Dynamics is the world's most sophisticated software and research company for advanced options analytics. Our volatility fitter and ultra-fast option pricers are the market standard, powering decisions at the world’s leading hedge funds, proprietary trading firms, market makers, and global banks.

We're seeking our first Data Engineer who will play a pivotal role in designing and developing our next-generation analytics infrastructure from the ground up. This is your chance to design and build a modern data platform from scratch—one that will power cutting-edge research into real-time and historical options analytics and volatility modeling.

We’re a tight-knit, highly technical team that values intellectual rigor, collaboration, and doing good work. You’ll take on broad responsibilities and contribute to work with real-world impact. We help each other grow, share ideas openly, and work together to get things done.

You’ll collaborate closely with our quantitative researchers to:

• Develop scalable pipelines to ingest, transform, and analyze real-time and historical options market data.

• Build, deploy, and maintain highly scalable, fault-tolerant data pipelines on our distributed computing infrastructure.

• Create intuitive internal tools, real-time dashboards, and custom reporting to accelerate research and streamline deployment into production.

Our data stack is built using Python and includes Dagster, Clickhouse, Kubernetes, Polars, and Grafana.

Who You Are

• You earned a BS or MS degree in Computer Science, Physics, Mathematics, Data Science, Engineering, or another quantitative discipline.

• You have between 2 years and 6 years of experience building, deploying, and maintaining robust production-grade data pipelines.

• You have hands-on experience deploying orchestration frameworks and containerization technologies such as Dagster, Airflow, Prefect, Docker, or Kubernetes.

• You are skilled at understanding, transforming, analyzing, visualizing, and debugging large numerical datasets.

• You are proficient in leveraging the scientific Python ecosystem—including Polars, NumPy, Matplotlib, and Jupyter—to explore, analyze, and visualize data effectively.

• You are a confident and effective communicator, capable of independently writing clear, concise documentation and presenting research to your colleagues.

• You consistently apply software and data engineering best practices, including version control, automated testing, and thorough documentation.

• You may have prior industry experience in options market making or derivatives modeling (5 years or less) but this is not required.

• You hold current authorization to work in the US.