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Found 18 Skills
Use this skill when designing data warehouses, building star or snowflake schemas, implementing slowly changing dimensions (SCDs), writing analytical SQL for Snowflake or BigQuery, creating fact and dimension tables, or planning ETL/ELT pipelines for analytics. Triggers on dimensional modeling, surrogate keys, conformed dimensions, warehouse architecture, data vault, partitioning strategies, materialized views, and any task requiring OLAP schema design or warehouse query optimization.
End-to-end retail ETL pipeline using PySpark, SQL Server, and Medallion Architecture (Bronze/Silver/Gold layers) for data warehousing
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
Use when "data pipelines", "ETL", "data warehousing", "data lakes", or asking about "Airflow", "Spark", "dbt", "Snowflake", "BigQuery", "data modeling"
Use this skill when building dbt models, designing semantic layers, defining metrics, creating self-serve analytics, or structuring a data warehouse for analyst consumption. Triggers on dbt project setup, model layering (staging, intermediate, marts), ref() and source() usage, YAML schema definitions, metrics definitions, semantic layer configuration, dimensional modeling, slowly changing dimensions, data testing, and any task requiring analytics engineering best practices.
Master data engineering, ETL/ELT, data warehousing, SQL optimization, and analytics. Use when building data pipelines, designing data systems, or working with large datasets.
Reference portfolio demonstrating Azure data engineering patterns, Medallion architecture, and end-to-end analytics solutions
Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation
Builds data infrastructure — ETL/ELT pipelines, data warehousing, stream processing, data quality, orchestration (Airflow/Dagster), and analytics engineering (dbt). Use when the user asks to build data pipelines, set up ETL/ELT workflows, design a data warehouse, configure stream processing, or implement analytics engineering with dbt, Airflow, or Dagster.
Expert in data pipelines, ETL processes, and data infrastructure
Google BigQuery for analytics, ML, and data warehousing. Use for large-scale analytics.
Comprehensive study guide covering data engineering concepts, tools, and best practices for learning and reference