Here's the fundamental Data Engineering stack you need to master, no matter the company you're aiming for.
Layer 1: Data Modeling & Schema Design
The foundation everything builds on.
- Normalization vs denormalization tradeoffs.
- Star and snowflake schemas.
- Slowly changing dimensions.
- Partitioning and bucketing strategies.
Poor modeling? Your queries will never scale.
Layer 2: SQL & Query Optimization
Your primary language for data.
- Complex joins and window functions.
- Query execution plans and indexes.
- Subquery vs CTE performance.
- Aggregation optimization techniques.
Can't write efficient SQL? You won't pass the technical.
Layer 3: Distributed Systems Fundamentals
How data systems actually work at scale.
- CAP theorem and consistency models.
- Partitioning and replication strategies.
- Distributed query processing.
- Fault tolerance and recovery.
Miss these concepts? You can't reason about production issues.
Layer 4: Data Pipeline Architecture
Moving data reliably at scale.
- Batch vs streaming tradeoffs.
- Idempotency and exactly-once processing.
- Backfill strategies and data quality.
- Orchestration and dependency management.
Bad pipelines? Data teams lose trust in your work.
Layer 5: Storage Systems & Formats
Where and how you store matters.
- Row vs columnar storage tradeoffs.
- Parquet, ORC, Avro characteristics.
- Data lake vs warehouse patterns.
- Compression and encoding strategies.
Wrong storage choices kill query performance.
Layer 6: Data Quality & Observability
Production data is messy.
- Schema validation and evolution.
- Data lineage and impact analysis.
- Monitoring pipeline health.
- SLA definition and alerting.
No observability? You're flying blind in production.
Layer 7: Performance & Scalability
The difference between junior and senior.
- Understanding data skew and hotspots.
- Memory vs disk tradeoffs.
- Caching strategies and materialization.
- Cost optimization techniques.
From Blogger iPhone client