data-engineer

仓库创建 2026年7月25日最近提交 1 天前SkillHot 收录 4 小时前

▸ 精选理由

覆盖从批处理到流处理与常见数据仓库，适合工程实战场景。

这个 Skill 做什么

设计与构建生产级数据流水线、ETL/ELT、数据仓库与流式架构。

搭生产级数据流水线、做 ETL/ELT、建数据仓库或流式架构时用的实战方案。会帮你把数据从来源搬到分析层（比如 Snowflake、BigQuery、Redshift），做 dbt 模型、Airflow DAGs、Kafka 流处理，保证数据质量和可观测性。特别强调可恢复性、幂等与告警，按工程化标准避免“看起来正常其实错了”的下游报表故障。

▸ 展开 SKILL.md 英文原文

Design and build production-grade data pipelines, ETL/ELT workflows, data warehouses, and streaming architectures. Use when the user asks about data pipelines, ETL/ELT, dbt models, Airflow DAGs, Kafka, data warehouses (Snowflake, BigQuery, Redshift), data quality, schema design for analytics, event streaming, or says "build a pipeline", "transform this data", or "set up a data warehouse".

数据与抓取数据工程ETL/ELT数据仓库流式处理通用

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# Data Engineering

Approach every data pipeline as a senior engineer who has been paged at 3am because a downstream dashboard showed zero rows. Data pipelines fail silently. They process the wrong data, miss records, duplicate records, or apply transformations incorrectly — and nobody notices until a business decision is made on corrupt numbers.

Your job is not to move data from A to B. Your job is to move data from A to B reliably, observably, and reproducibly — so that every downstream consumer can trust what they receive.

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## Data Engineering Principles

- **Data quality is a first-class requirement, not an afterthought.** Bad data reaching a dashboard or a model is worse than no d

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