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-Python packages
-Cloud technologies
-maybe metadata / pre-processing ?
Java, Spark, distributed computing, streaming
Some orgs still use "data architect" instead of engineer. Really depends on the pipeline: some are thrilled if you can handle SQL > Python > AWS
Depending on how ambitious the role is, you'll want to throw in some of the fundamentals of building scalable infra across the tech stack to show you're serious. Full-stack SWEs get the most job offers.
Don't bluff; you'll need to prepped for code testing in anything you mention.
For example across a truly productized data stack:
- Version control/collab: Git and Gitlab
- Development environments, cf Jupyter Notebooks
- Server architecture: AWS or other Cloud
- Middleware: JavaScript, Flask/Django w Python or whatever packages that support web frontend apps in whatever language
- Deployment, cf Heroku
- DevOps