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Found 68 Skills
Reference portfolio demonstrating Azure data engineering patterns, Medallion architecture, and end-to-end analytics solutions
Claude Code skills for analytics and data engineers working with dbt, Snowflake, and data pipelines
Comprehensive study guide covering data engineering concepts, tools, and best practices for learning and reference
Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation
End-to-end data engineering pipeline using MinIO, Airbyte, PostgreSQL, DBT, and Airflow with medallion architecture (Bronze/Silver/Gold layers)
End-to-end data engineering and analytics application using Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization
Generates the Data Engineering team weekly/sprint status report from Azure DevOps. Organized by Epic, with objectives, on/off track status, risks, completed and planned items per area, clickable ADO links, and unplanned items flagged. Use when the user asks for a Data Engineering sprint report, weekly report, current sprint status, activities report, or sprint summary.
End-to-end ETL pipeline for Harvard Art Museums API with SQL analytics and Streamlit visualization
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
Native Arrow filesystem integration with PyArrow. Optimized for Parquet workflows, zero-copy data transfer, predicate pushdown, and column pruning. Covers S3, GCS, HDFS with PyArrow datasets.
Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.