Complete overhaul of senior-data-engineer skill (previously Grade F: 43/100): SKILL.md (~550 lines): - Added table of contents and trigger phrases - 3 actionable workflows: Batch ETL Pipeline, Real-Time Streaming, Data Quality Framework - Architecture decision framework (Batch vs Stream, Lambda vs Kappa) - Tech stack overview with decision matrix - Troubleshooting section with common issues and solutions Reference Files (all rewritten from 81-line boilerplate): - data_pipeline_architecture.md (~700 lines): Lambda/Kappa architectures, batch processing with Spark, stream processing with Kafka/Flink, exactly-once semantics, error handling strategies, orchestration patterns - data_modeling_patterns.md (~650 lines): Dimensional modeling (Star/Snowflake/OBT), SCD Types 0-6 with SQL implementations, Data Vault (Hub/Satellite/Link), dbt best practices, partitioning and clustering strategies - dataops_best_practices.md (~750 lines): Data testing (Great Expectations, dbt), data contracts with YAML definitions, CI/CD pipelines, observability with OpenLineage, incident response runbooks, cost optimization Python Scripts (all rewritten from 101-line placeholders): - pipeline_orchestrator.py (~600 lines): Generates Airflow DAGs, Prefect flows, and Dagster jobs with configurable ETL patterns - data_quality_validator.py (~1640 lines): Schema validation, data profiling, Great Expectations suite generation, data contract validation, anomaly detection - etl_performance_optimizer.py (~1680 lines): SQL query analysis, Spark job optimization, partition strategy recommendations, cost estimation for BigQuery/Snowflake/Redshift/Databricks Resolves #53 Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Alireza Rezvani
GitHub