A Data Engineer, Scientist and Analyst Walk Into a Bar
summary
Defines concrete handoffs: engineers build ingestion pipelines, deploy automated CI/CD and run cloud infra on Databricks or Azure/AWS; analysts clean data, build dashboards in Tableau and own data models; scientists spend ~three hours coding, use Python for feature engineering and ML, then productionize with MLOps. Examples include building lakehouses and automating repeatable ETL, adding sensors to kegs and training predictive models to forecast failures, and keeping documentation so future teams can pick up and iterate.
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chapters (11)
- 0:00Three data roles
- 3:19Data engineer daily tasks
- 11:03Data analyst core duties
- 18:52Data scientist journey
- 28:26Role overlaps and differences
- 32:21Bar scenario problem-solving
- 35:48Career growth resources
- 37:53Cloud for data engineers
- 40:04Automation tools trade-offs
- 41:09Data governance strategies
- 43:35Importance of documentation
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