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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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Fighting fraud with engineering and machine learning - Angela Ramirez

Outline:

• Data engineering
• ML for Fraud protection

About the speaker: Angela is a data engineer working at Sam’s Club with their fraud team. She has 4 years of experience as a data engineer. Currently specialising in machine learning for fraud prevention, Angela works on designing and maintaining the data for a machine learning system identifying fraudulent transactions.

In addition, Angela has a masters in Natural Language Processing where she focused on coreference resolution and dialogue systems. Her most recent research includes generating quality synthetic data from LLMs for dialogue systems.

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