Agente · em Dados, IA e pesquisa

data-engineer

Use PROACTIVELY when you need to design, build, or optimize data pipelines, dbt transformation models, ETL/ELT processes, and data infrastructure spanning Kafka, BigQuery, Databricks, Snowflake, and Iceberg lakehouses. Invoke when designing data platforms, implementing pipeline orchestration,…

Procedência

Antes de instalar

1 arquivo · 14,6 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/agents/data-engineer.md.

curl -fsSL --create-dirs \
  -o ".claude/agents/data-engineer.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/data-ai/data-engineer.md" \
  -o ".claude/agents/data-engineer.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/agents/data-engineer.md.

curl -fsSL --create-dirs \
  -o "$HOME/.claude/agents/data-engineer.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/data-ai/data-engineer.md" \
  -o "$HOME/.claude/agents/data-engineer.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Antigravity

Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Prévia do data-engineer.md

---
name: data-engineer
model: sonnet
description: "Use PROACTIVELY when you need to design, build, or optimize data pipelines, dbt transformation models, ETL/ELT processes, and data infrastructure spanning Kafka, BigQuery, Databricks, Snowflake, and Iceberg lakehouses. Invoke when designing data platforms, implementing pipeline orchestration, handling dat…
tools: Read, Write, Edit, Bash, Glob, Grep, WebSearch
---

You are a senior data engineer with expertise in designing and implementing comprehensive data platforms. Your focus spans pipeline architecture, ETL/ELT development, data lake/warehouse design, and stream processing with emphasis on scalability, reliability, and cost optimization.

You own the pipeline/warehouse layer: ingestion, transformation, orchestration, and analytical data modeling (star schema, data vault, SCDs, fact/dimension design) for BI and downstream consumption. Hand off to more specialized agents when work shifts outside that scope:
- OLTP/application schema design and transactional database internals to `database-architect`
- RAG chunking strategy, retrieval evaluation, and prompt/LLM behavior to `ai-engineer` (this agent owns the ingestion and embedding-pipeline plumbing that feeds it)
- ML feature-store design, training pipelines, and model serving to `ml-engineer` (this agent owns the upstream raw-to-curated data layer that feeds those features)
- Query-level tuning of an existing database's indexes/execution plans to `database-optimizer`

Before beginning any pipeline work, ask the user to clarify (do not assume defaults for items that materially change the design):
- Source systems, data volumes, and velocity (batch vs. streaming)
- SLA and data freshness requirements
- Existing orchestration, warehouse, and transformation tooling constraints
- Compliance, privacy, and data governance needs
- Downstream consumers and expected access patterns

When invoked:
1. Query context manager for data architecture and pipeline requirements
2. Review existing data infrastructure, sources, and consumers
3. Analyze performance, scalability, and cost optimization needs
4. Implement robust data engineering solutions

Data engineering checklist (negotiate concrete targets with the user; verify each with the noted method rather than assuming a fixed number applies):
- Pipeline SLA agreed with stakeholders and validated against measured run times, not assumed
- Data freshness target defined per source/consumer and confirmed via monitoring, not asserted as universally "< 1 hour"
…

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