[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6NTWPA09R3R30MT2APX02WR":3,"skill-same:01M6NTWPA09R3R30MT2APX02WR":106},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6NTWPA09R3R30MT2APX02WR","senior-data-engineer",null,"Data engineering skill for building scalable data pipelines, ETL\u002FELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.","mattonweb\u002Fai-assistant-foundry","mattonweb","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F2309734?v=4","01M6NQYCY846TKYN2ZCRS71WQ7","Matt Johnson","01M6NQYD2NPAQEFVD8TW9W0SVX",1,"MIT","https:\u002F\u002Fgithub.com\u002Fmattonweb\u002Fai-assistant-foundry\u002Fblob\u002Fmain\u002Fdata-engineering\u002Falirezarezvani\u002Fsenior-data-engineer\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"engineer","senior data engineer","data","data engineer","expertise","orchestration","implementing","Scalable","modeling","stack","pipeline","etl","elt","data pipelines","data engineering","airflow","issues","dataops","sql","workflows","2026-11-10 10:12:30",false,"---\nname: \"senior-data-engineer\"\ndescription: Data engineering skill for building scalable data pipelines, ETL\u002FELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.\n---\n\n# Senior Data Engineer\n\nProduction-grade data engineering skill for building scalable, reliable data systems.\n\n## Table of Contents\n\n1. [Trigger Phrases](#trigger-phrases)\n2. [Quick Start](#quick-start)\n3. [Workflows](#workflows)\n   - [Building a Batch ETL Pipeline](#workflow-1-building-a-batch-etl-pipeline)\n   - [Implementing Real-Time Streaming](#workflow-2-implementing-real-time-streaming)\n   - [Data Quality Framework Setup](#workflow-3-data-quality-framework-setup)\n4. [Architecture Decision Framework](#architecture-decision-framework)\n5. [Tech Stack](#tech-stack)\n6. [Reference Documentation](#reference-documentation)\n7. [Troubleshooting](#troubleshooting)\n\n---\n\n## Trigger Phrases\n\nActivate this skill when you see:\n\n**Pipeline Design:**\n- \"Design a data pipeline for...\"\n- \"Build an ETL\u002FELT process...\"\n- \"How should I ingest data from...\"\n- \"Set up data extraction from...\"\n\n**Architecture:**\n- \"Should I use batch or streaming?\"\n- \"Lambda vs Kappa architecture\"\n- \"How to handle late-arriving data\"\n- \"Design a data lakehouse\"\n\n**Data Modeling:**\n- \"Create a dimensional model...\"\n- \"Star schema vs snowflake\"\n- \"Implement slowly changing dimensions\"\n- \"Design a data vault\"\n\n**Data Quality:**\n- \"Add data validation to...\"\n- \"Set up data quality checks\"\n- \"Monitor data freshness\"\n- \"Implement data contracts\"\n\n**Performance:**\n- \"Optimize this Spark job\"\n- \"Query is running slow\"\n- \"Reduce pipeline execution time\"\n- \"Tune Airflow DAG\"\n\n---\n\n## Quick Start\n\n### Core Tools\n\n```bash\n\n# Generate pipeline orchestration config\npython scripts\u002Fpipeline_orchestrator.py generate \\\n  --type airflow \\\n  --source postgres \\\n  --destination snowflake \\\n  --schedule \"0 5 * * *\"\n\n# Validate data quality\npython scripts\u002Fdata_quality_validator.py validate \\\n  --input data\u002Fsales.parquet \\\n  --schema schemas\u002Fsales.json \\\n  --checks freshness,completeness,uniqueness\n\n# Optimize ETL performance\npython scripts\u002Fetl_performance_optimizer.py analyze \\\n  --query queries\u002Fdaily_aggregation.sql \\\n  --engine spark \\\n  --recommend\n```\n\n---\n\n## Workflows\n→ See references\u002Fworkflows.md for details\n\n## Architecture Decision Framework\n\nUse this framework to choose the right approach for your data pipeline.\n\n### Batch vs Streaming\n\n| Criteria | Batch | Streaming |\n|----------|-------|-----------|\n| **Latency requirement** | Hours to days | Seconds to minutes |\n| **Data volume** | Large historical datasets | Continuous event streams |\n| **Processing complexity** | Complex transformations, ML | Simple aggregations, filtering |\n| **Cost sensitivity** | More cost-effective | Higher infrastructure cost |\n| **Error handling** | Easier to reprocess | Requires careful design |\n\n**Decision Tree:**\n```\nIs real-time insight required?\n├── Yes → Use streaming\n│   └── Is exactly-once semantics needed?\n│       ├── Yes → Kafka + Flink\u002FSpark Structured Streaming\n│       └── No → Kafka + consumer groups\n└── No → Use batch\n    └── Is data volume > 1TB daily?\n        ├── Yes → Spark\u002FDatabricks\n        └── No → dbt + warehouse compute\n```\n\n### Lambda vs Kappa Architecture\n\n| Aspect | Lambda | Kappa |\n|--------|--------|-------|\n| **Complexity** | Two codebases (batch + stream) | Single codebase |\n| **Maintenance** | Higher (sync batch\u002Fstream logic) | Lower |\n| **Reprocessing** | Native batch layer | Replay from source |\n| **Use case** | ML training + real-time serving | Pure event-driven |\n\n**When to choose Lambda:**\n- Need to train ML models on historical data\n- Complex batch transformations not feasible in streaming\n- Existing batch infrastructure\n\n**When to choose Kappa:**\n- Event-sourced architecture\n- All processing can be expressed as stream operations\n- Starting fresh without legacy systems\n\n### Data Warehouse vs Data Lakehouse\n\n| Feature | Warehouse (Snowflake\u002FBigQuery) | Lakehouse (Delta\u002FIceberg) |\n|---------|-------------------------------|---------------------------|\n| **Best for** | BI, SQL analytics | ML, unstructured data |\n| **Storage cost** | Higher (proprietary format) | Lower (open formats) |\n| **Flexibility** | Schema-on-write | Schema-on-read |\n| **Performance** | Excellent for SQL | Good, improving |\n| **Ecosystem** | Mature BI tools | Growing ML tooling |\n\n---\n\n## Tech Stack\n\n| Category | Technologies |\n|----------|--------------|\n| **Languages** | Python, SQL, Scala |\n| **Orchestration** | Airflow, Prefect, Dagster |\n| **Transformation** | dbt, Spark, Flink |\n| **Streaming** | Kafka, Kinesis, Pub\u002FSub |\n| **Storage** | S3, GCS, Delta Lake, Iceberg |\n| **Warehouses** | Snowflake, BigQuery, Redshift, Databricks |\n| **Quality** | Great Expectations, dbt tests, Monte Carlo |\n| **Monitoring** | Prometheus, Grafana, Datadog |\n\n---\n\n## Reference Documentation\n\n### 1. Data Pipeline Architecture\nSee `references\u002Fdata_pipeline_architecture.md` for:\n- Lambda vs Kappa architecture patterns\n- Batch processing with Spark and Airflow\n- Stream processing with Kafka and Flink\n- Exactly-once semantics implementation\n- Error handling and dead letter queues\n\n### 2. Data Modeling Patterns\nSee `references\u002Fdata_modeling_patterns.md` for:\n- Dimensional modeling (Star\u002FSnowflake)\n- Slowly Changing Dimensions (SCD Types 1-6)\n- Data Vault modeling\n- dbt best practices\n- Partitioning and clustering\n\n### 3. DataOps Best Practices\nSee `references\u002Fdataops_best_practices.md` for:\n- Data testing frameworks\n- Data contracts and schema validation\n- CI\u002FCD for data pipelines\n- Observability and lineage\n- Incident response\n\n---\n\n## Troubleshooting\n→ See references\u002Ftroubleshooting.md for details\n","# Senior Data Engineer\n\nProduction-grade data engineering skill for building scalable, reliable data systems.\n\n## Table of Contents\n\n1. [Trigger Phrases](#trigger-phrases)\n2. [Quick Start](#quick-start)\n3. [Workflows](#workflows)\n   - [Building a Batch ETL Pipeline](#workflow-1-building-a-batch-etl-pipeline)\n   - [Implementing Real-Time Streaming](#workflow-2-implementing-real-time-streaming)\n   - [Data Quality Framework Setup](#workflow-3-data-quality-framework-setup)\n4. [Architecture Decision Framework](#architecture-decision-framework)\n5. [Tech Stack](#tech-stack)\n6. [Reference Documentation](#reference-documentation)\n7. [Troubleshooting](#troubleshooting)\n\n---\n\n## Trigger Phrases\n\nActivate this skill when you see:\n\n**Pipeline Design:**\n- \"Design a data pipeline for...\"\n- \"Build an ETL\u002FELT process...\"\n- \"How should I ingest data from...\"\n- \"Set up data extraction from...\"\n\n**Architecture:**\n- \"Should I use batch or streaming?\"\n- \"Lambda vs Kappa architecture\"\n- \"How to handle late-arriving data\"\n- \"Design a data lakehouse\"\n\n**Data Modeling:**\n- \"Create a dimensional model...\"\n- \"Star schema vs snowflake\"\n- \"Implement slowly changing dimensions\"\n- \"Design a data vault\"\n\n**Data Quality:**\n- \"Add data validation to...\"\n- \"Set up data quality checks\"\n- \"Monitor data freshness\"\n- \"Implement data contracts\"\n\n**Performance:**\n- \"Optimize this Spark job\"\n- \"Query is running slow\"\n- \"Reduce pipeline execution time\"\n- \"Tune Airflow DAG\"\n\n---\n\n## Quick Start\n\n### Core Tools\n\n```bash\n\n# Generate pipeline orchestration config\npython scripts\u002Fpipeline_orchestrator.py generate \\\n  --type airflow \\\n  --source postgres \\\n  --destination snowflake \\\n  --schedule \"0 5 * * *\"\n\n# Validate data quality\npython scripts\u002Fdata_quality_validator.py validate \\\n  --input data\u002Fsales.parquet \\\n  --schema schemas\u002Fsales.json \\\n  --checks freshness,completeness,uniqueness\n\n# Optimize ETL performance\npython scripts\u002Fetl_performance_optimizer.py analyze \\\n  --query queries\u002Fdaily_aggregation.sql \\\n  --engine spark \\\n  --recommend\n```\n\n---\n\n## Workflows\n→ See references\u002Fworkflows.md for details\n\n## Architecture Decision Framework\n\nUse this framework to choose the right approach for your data pipeline.\n\n### Batch vs Streaming\n\n| Criteria | Batch | Streaming |\n|----------|-------|-----------|\n| **Latency requirement** | Hours to days | Seconds to minutes |\n| **Data volume** | Large historical datasets | Continuous event streams |\n| **Processing complexity** | Complex transformations, ML | Simple aggregations, filtering |\n| **Cost sensitivity** | More cost-effective | Higher infrastructure cost |\n| **Error handling** | Easier to reprocess | Requires careful design |\n\n**Decision Tree:**\n```\nIs real-time insight required?\n├── Yes → Use streaming\n│   └── Is exactly-once semantics needed?\n│       ├── Yes → Kafka + Flink\u002FSpark Structured Streaming\n│       └── No → Kafka + consumer groups\n└── No → Use batch\n    └── Is data volume > 1TB daily?\n        ├── Yes → Spark\u002FDatabricks\n        └── No → dbt + warehouse compute\n```\n\n### Lambda vs Kappa Architecture\n\n| Aspect | Lambda | Kappa |\n|--------|--------|-------|\n| **Complexity** | Two codebases (batch + stream) | Single codebase |\n| **Maintenance** | Higher (sync batch\u002Fstream logic) | Lower |\n| **Reprocessing** | Native batch layer | Replay from source |\n| **Use case** | ML training + real-time serving | Pure event-driven |\n\n**When to choose Lambda:**\n- Need to train ML models on historical data\n- Complex batch transformations not feasible in streaming\n- Existing batch infrastructure\n\n**When to choose Kappa:**\n- Event-sourced architecture\n- All processing can be expressed as stream operations\n- Starting fresh without legacy systems\n\n### Data Warehouse vs Data Lakehouse\n\n| Feature | Warehouse (Snowflake\u002FBigQuery) | Lakehouse (Delta\u002FIceberg) |\n|---------|-------------------------------|---------------------------|\n| **Best for** | BI, SQL analytics | ML, unstructured data |\n| **Storage cost** | Higher (proprietary format) | Lower (open formats) |\n| **Flexibility** | Schema-on-write | Schema-on-read |\n| **Performance** | Excellent for SQL | Good, improving |\n| **Ecosystem** | Mature BI tools | Growing ML tooling |\n\n---\n\n## Tech Stack\n\n| Category | Technologies |\n|----------|--------------|\n| **Languages** | Python, SQL, Scala |\n| **Orchestration** | Airflow, Prefect, Dagster |\n| **Transformation** | dbt, Spark, Flink |\n| **Streaming** | Kafka, Kinesis, Pub\u002FSub |\n| **Storage** | S3, GCS, Delta Lake, Iceberg |\n| **Warehouses** | Snowflake, BigQuery, Redshift, Databricks |\n| **Quality** | Great Expectations, dbt tests, Monte Carlo |\n| **Monitoring** | Prometheus, Grafana, Datadog |\n\n---\n\n## Reference Documentation\n\n### 1. Data Pipeline Architecture\nSee `references\u002Fdata_pipeline_architecture.md` for:\n- Lambda vs Kappa architecture patterns\n- Batch processing with Spark and Airflow\n- Stream processing with Kafka and Flink\n- Exactly-once semantics implementation\n- Error handling and dead letter queues\n\n### 2. Data Modeling Patterns\nSee `references\u002Fdata_modeling_patterns.md` for:\n- Dimensional modeling (Star\u002FSnowflake)\n- Slowly Changing Dimensions (SCD Types 1-6)\n- Data Vault modeling\n- dbt best practices\n- Partitioning and clustering\n\n### 3. 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