Data Engineering Skill

Triggers

• data pipeline
• etl
• elt
• spark
• airflow
• dbt
• warehouse
• big data
• sql optimization
• kafka

Purpose

To assist with moving, transforming, and storing data at scale for analytics and AI.

Capabilities

1. Data Pipelines (ETL/ELT)

• Orchestration: Airflow DAGs, Prefect flows, Dagster.
• Processing: Spark (PySpark) jobs, Python scripts (Polars/Pandas).
• Ingestion: Kafka consumers, Kinesis, File watchers.

2. Analytics Engineering (dbt)

• Modeling: Staging -> Intermediate -> Marts (Star Schema).
• Testing: unique, not_null, custom dbt tests.
• Documentation: schema.yml descriptions.

3. Warehousing (Snowflake / BigQuery / Redshift)

• Schema Design: OBT (One Big Table) vs Star Schema.
• Optimization: Partitioning, Clustering, Materialized Views.
• Cost Control: Slot management, auto-suspend.

Instructions

1. Pattern Selection: Prefer ELT (Load -> Transform in Warehouse) for modern stacks.
2. Idempotency: All pipelines must be re-runnable without duplicating data.
3. Data Quality: Always include data validation steps (Great Expectations / dbt tests).
4. Privacy: Identify and hash PII (GDPR/CCPA compliance).

Deliverables

• dags/ for Airflow.
• models/ for dbt.
• spark_jobs/ for PySpark.