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Analytics Engineer

Bridging the gap between data engineering and data analysis — building reliable, tested, documented data pipelines.

Pages

  • Fundamentals — what is AE, ELT vs ETL, SQL for analytics, PostgreSQL functions, performance tuning.
  • Data Modeling — data warehouse fundamentals, OLTP/OLAP, dimensional modeling, medallion architecture, star/snowflake schema, grain.
  • ELT Process — extract-load-transform pipeline, staging layer, cleaning and standardization.
  • dbt — projects, models, seeds, sources, DAG, materialization, philosophy.
  • Version Control — Git cheatsheet for analytics workflows.
  • ADLC — the Analytics Development Lifecycle (plan, develop, test, deploy, operate, discover).
  • Materials