The financial industry has long been driven by data, requiring a mature and robust capacity for discovering and integrating valuable sources of information. Citadel is no exception, and in this episode Michael Watson and Robert Krzyzanowski share their experiences managing and leading the data engineering teams that power the business. They shared helpful insights into some of the challenges associated with working in a regulated industry, organizing teams to deliver value rapidly and reliably, and how they approach career development for data engineers. This was a great conversation for an inside look at how to build and maintain a data driven culture.
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- Your host is Tobias Macey and today I’m interviewing Michael Watson and Robert Krzyzanowski about the technical and organizational challenges that he and his team are working on at Citadel
- How did you get involved in the area of data management?
- Can you start by describing the size and structure of the data engineering teams at Citadel?
- How have the scope and nature of responsibilities for data engineers evolved over the past few years at Citadel as more and better tools and platforms have been made available in the space and machine learning techniques have grown more sophisticated?
- Can you describe the types of data that you are working with at Citadel?
- What is the process for identifying, evaluating, and ingesting new sources of data?
- What are some of the common core aspects of your data infrastructure?
- What are some of the ways that it differs across teams or projects?
- How involved are data engineers in the overall product design and delivery lifecycle?
- For someone who joins your team as a data engineer, what are some of the options available to them for a career path?
- What are some of the challenges that you are currently facing in managing the data lifecycle for projects at Citadel?
- What are some tools or practices that you are excited to try out?
- @detroitcoder on Twitter
- detroitcoder on GitHub
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- Hedge Fund
- Quantitative Trading
- Citadel Securities
- Apache Airflow
- Jupyter Hub
- Alembic database migrations for SQLAlchemy
- DQM == Data Quality Management
- Great Expectations
- Active Directory
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA