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Collection · 12 skills

Data Engineering Stack

From raw source to trusted dashboard: orchestrate idempotent pipelines, model the warehouse with clear grain, transform with dbt or Spark, test data like code, write analytical SQL that answers the real question, and publish dashboards people believe.

Best for Analytics and data engineers building the warehouse and its pipelines.

What's inside

Listed in the order we suggest adopting them. You don't call them by name: each skill loads on its own when a request matches it.

  1. Data Pipelines Rerun any day, get the same data Data Engineering data-pipelines 203 lines
  2. dbt Models Declared grain, tested keys, idempotent increments Data Engineering dbt-models 257 lines
  3. Dimensional Modeling Declare the grain, conform the dimensions Data Engineering dimensional-modeling 181 lines
  4. PySpark Jobs Fewer shuffles, no stragglers, right-sized files Data Engineering pyspark-jobs 187 lines
  5. Data Quality Checks Catch bad data before the dashboard does Data Engineering data-quality-checks 219 lines
  6. Analytics SQL Stated grain, no fan-out, numbers that reconcile Data Engineering analytics-sql 171 lines
  7. Google BigQuery Scan less, pay less, know who spent what Cloud Platforms gcp-bigquery 235 lines
  8. DuckDB Analytics SQL over Parquet and CSV files, no warehouse needed Databases duckdb-analytics 219 lines
  9. BI Dashboards One definition per number, one owner per dashboard Data Engineering bi-dashboards 148 lines
  10. Data Anonymization Share data without sharing the people in it Data Engineering data-anonymization 184 lines
  11. Airflow DAGs Airflow 3 DAGs that parse fast and rerun cleanly Data Engineering airflow-dags 318 lines
  12. Lakehouse with Apache Iceberg One catalog, pruned scans, maintenance on a schedule Data Engineering lakehouse-iceberg 265 lines

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