2 min lesson
The named stack
Choose two examples from the table in "The named stack" and explain what each teaches you to do.
Step 1 of 3
The named stackwhat to be fluent in
The JD names specific tools. Treat the named ones as table stakes and the "strong plus" as the differentiator worth investing in before the loop.
- Layer
- Lakehouse / compute
- Named in JD
- Databricks, Delta Lake, Spark
- How hard a gate
- Core - expect internals questions, not just usage
- Layer
- Orchestration
- Named in JD
- Dagster
- How hard a gate
- "Strong plus" - the clearest single edge you can build
- Layer
- Tooling layer
- Named in JD
- BI, catalog, ingestion connectors, reverse-ETL
- How hard a gate
- You help choose - bring build-vs-buy opinions
- Layer
- Languages
- Named in JD
- Strong SQL and Python
- How hard a gate
- Hard gate - tested live on the first technical screen
| Layer | Named in JD | How hard a gate |
|---|---|---|
| Lakehouse / compute | Databricks, Delta Lake, Spark | Core - expect internals questions, not just usage |
| Orchestration | Dagster | "Strong plus" - the clearest single edge you can build |
| Tooling layer | BI, catalog, ingestion connectors, reverse-ETL | You help choose - bring build-vs-buy opinions |
| Languages | Strong SQL and Python | Hard gate - tested live on the first technical screen |
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Full explanation
Builders, not button-clickers
Builders, not button-clickersthe experience bar
The requirement for "low-level, from-scratch implementation of a modern data stack" is doing real work in that sentence. They want engineers who understand what Spark does under the hood, not operators who only configure managed services.
4+ years of full-time data platform engineering and proven ingestion-at-scale experience are screening filters, not preferences. If your background is running other people's jobs rather than building the platform, lead with the from-scratch work you have done - a connector you wrote, a partitioning scheme you designed, a cost regression you chased to its root.