Data Engineering for Fraud Prevention - Angela Ramirez

We talked about:

• Angela's background
• Angela's role at Sam's Club
• The usefulness of knowing ML as a data engineer
• Angela's career path
• Transitioning from data analyst to data engineer/system designer
• Best practices for system design and data engineering
• Working with document databases
• Working with network-based databases
• Detecting fraud with a network-based database
• Selecting the database type to work with
• Neo4j vs Postgres
• The importance of having software engineering knowledge in data engineering
• Data quality check tooling
• The greatest challenges in data engineering
• Debugging and finding the root cause of a failed job
• What kinds of tools Angela uses on a daily basis
• Working with external data sources
• Angela's resource recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/aramirez1305/
• Twitter: https://twitter.com/angelamaria__r
• Github: https://github.com/aramir62
• Previous podcast talk: https://twitter.com/i/spaces/1OwGWwZAZDnGQ?s=20

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