claudecodex

data-engineering

ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development

0 on GitHubby Seth Hobsonv1.3.2MITupdated 3 months agodata
/

Plugin data-engineering from marketplace claude-code-workflows in RUSHYOP/mirror-wshobson-agents. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (4)

  • airflow-dag-patterns

    Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

  • data-quality-frameworks

    Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

  • dbt-transformation-patterns

    Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.

  • spark-optimization

    Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

Slash commands (2)

  • /data-engineering:data-driven-feature

    Build features guided by data insights, A/B testing, and continuous measurement

  • /data-engineering:data-pipeline

Subagents (2)

  • data-engineering-backend-architect

    Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems. Masters REST/GraphQL/gRPC APIs, event-driven architectures, service mesh patterns, and modern backend frameworks. Handles service boundary definition, inter-service communication, resil

  • data-engineer

    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.

View source on GitHub

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