Data Engineer
Build the pipes data flows through: reliable pipelines, clean models, and warehouses analysts and ML can trust.
✦ Guide me on this path with AIRoadmap progress
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How to read the signals
Importance High Market demand Medium Automation risk Low
- 1
Advanced SQL
CTEs, window functions, query optimization. The data engineer's native tongue.
Importance High Market demand High Automation risk Low - 2
Python for pipelines
Batch jobs, APIs, file formats (Parquet). Glue that moves data reliably.
Importance High Market demand High Automation risk Medium - 3
Warehouses & lakehouses
BigQuery/Snowflake/Databricks. Where analytics data lands and scales.
Importance High Market demand High Automation risk Low - 4
ETL/ELT & orchestration
Airflow/dbt, scheduling, dependencies, retries, idempotency.
Importance High Market demand High Automation risk Medium - 5
Data modeling
Star schemas, normalization, slowly-changing dimensions. Clean models scale.
Importance High Market demand Medium Automation risk Low - 6
Streaming
Kafka, event-driven pipelines, exactly-once. Real time when the business needs it.
Importance Medium Market demand High Automation risk Low - 7
Quality & governance
Tests, lineage, cataloging, access control. Trust is the real deliverable.
Importance High Market demand High Automation risk Low