Data Engineering Basics
Moving and shaping data for analytics.
Backend Engineer track
Junior
Write correct code, ship small changes safely, ask good questions.
Nothing here yet.
Mid-level
Own a feature end to end without hand-holding.
- Analytics Event TrackingRecording user actions for later analysis.
- Data PipelineA sequence of steps that moves and transforms data.
- Data WarehouseA database optimized for analytics, like BigQuery or Snowflake.
- OLTP vs OLAPTransaction processing vs analytical queries.
Senior
Own a system, its failure modes, and its trade-offs.
- Columnar StorageStoring data by column for fast analytics.
- Data LakeCheap storage for raw data in any format.
- Data QualityMaking sure data is accurate, complete and fresh.
- dbtTransforming warehouse data with versioned SQL.
- ParquetA columnar file format for analytics.
- Pipeline Orchestration (Airflow)Scheduling and managing data pipelines.
Staff
Shape how many teams build, across systems.
- Reverse ETLSyncing warehouse data back into operational tools.
Principal
Set technical direction for the organization.
Nothing here yet.
Data Analyst track
Junior
Write correct SQL, build trusted dashboards, ask good questions.
- Data LakeCheap storage for raw data in any format.
- Data WarehouseA database optimized for analytics, like BigQuery or Snowflake.
Mid-level
Own an analysis end to end, from vague question to recommendation.
Core: start here
- dbtTransforming warehouse data with versioned SQL.
1 more mid-level concepts
- Data PipelineA sequence of steps that moves and transforms data.
Senior
Own experimentation and metrics design; call out bad numbers.
Nothing here yet.
Staff
Shape how the organization measures and decides.
Nothing here yet.
Principal
Set measurement strategy across the company.
Nothing here yet.
Data Engineer track
Junior
Build and fix pipelines from clear specs; write correct SQL.
- Analytics Event TrackingRecording user actions for later analysis.
- Columnar StorageStoring data by column for fast analytics.
- Data LakeCheap storage for raw data in any format.
- Data PipelineA sequence of steps that moves and transforms data.
- Data QualityMaking sure data is accurate, complete and fresh.
- Data WarehouseA database optimized for analytics, like BigQuery or Snowflake.
- dbtTransforming warehouse data with versioned SQL.
- OLTP vs OLAPTransaction processing vs analytical queries.
- ParquetA columnar file format for analytics.
- Pipeline Orchestration (Airflow)Scheduling and managing data pipelines.
Mid-level
Own pipelines and models end to end, including their quality.
- Reverse ETLSyncing warehouse data back into operational tools.
Senior
Design the platform's storage, processing and modeling choices.
Nothing here yet.
Staff
Shape how the whole organization produces and uses data.
Nothing here yet.
Principal
Set data strategy and architecture across the company.
Nothing here yet.
Frontend Engineer track
Junior
Build UI that works, ship small changes safely, ask good questions.
Nothing here yet.
Mid-level
Own a feature end to end without hand-holding.
- Analytics Event TrackingRecording user actions for later analysis.
Senior
Own an app's architecture, performance, and failure modes.
Nothing here yet.
Staff
Shape how many teams build, across apps.
Nothing here yet.
Principal
Set technical direction for the organization.
Nothing here yet.