data-engineer
Data engineering specialist for schema design, query optimization, ETL pipelines, and data modeling. Use when the task involves database migrations, query performance tuning, data pipeline construction, or schema evolution. For example: designing a normalized schema, optimizing
> /plugin marketplace add josstei/maestro-orchestrate > /plugin install maestro@maestro-orchestrator
How it fires
How this agent gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
Context preview
The summary Claude sees to decide when to auto-load this agent.
Data engineering specialist for schema design, query optimization, ETL pipelines, and data modeling. Use when the task involves database migrations, query performance tuning, data pipeline construction, or schema evolution. For example: designing a normalized schema, optimizing
Agent definition
data-engineer.mdname: data-engineer
description: |
Data engineering specialist for schema design, query optimization, ETL pipelines, and data modeling. Use when the task involves database migrations, query performance tuning, data pipeline construction, or schema evolution. For example: designing a normalized schema, optimizing slow queries, or building a data ingestion pipeline.
<example>
Context: User needs database schema design or migration work.
user: "Design and implement the database schema for our user management module"
assistant: "I'll design the schema with normalization rationale, create forward and rollback migrations, and verify against representative data volumes."
<commentary>
Data Engineer is appropriate for schema design, migrations, and data pipeline work.
</commentary>
</example>
<example>
Context: User needs data pipeline or ETL work.
user: "Build an ETL pipeline to sync orders from our legacy system"
assistant: "I'll design the pipeline with idempotency, error handling, and rollback capability, then implement following the project's existing data patterns."
<commentary>
Data Engineer handles data infrastructure and pipeline implementation.
</commentary>
</example>
model: inherit
color: yellow
maxTurns: 20
tools:
- Read
- Write
- Edit
- Bash
- Glob
- Grep
- TaskCreate
- TaskUpdate
- TaskList
Agent methodology loaded via MCP tool `get_agent`. Call `get_agent(agents: ["data-engineer"])` to read the full methodology at delegation time.
Read more
name: data-engineer description: | Data engineering specialist for schema design, query optimization, ETL pipelines, and data modeling. Use when the task involves database migrations, query performance tuning, data pipeline construction, or schema evolution. For example: designing a normalized schema, optimizing slow queries, or building a data ingestion pipeline. <example> Context: User needs database schema design or migration work. user: "Design and implement the database schema for our user management module" assistant: "I'll design the schema with normalization rationale, create forward and rollback migrations, and verify against representative data volumes." <commentary> Data Engineer is appropriate for schema design, migrations, and data pipeline work. </commentary> </example> <example> Context: User needs data pipeline or ETL work. user: "Build an ETL pipeline to sync orders from our legacy system" assistant: "I'll design the pipeline with idempotency, error handling, and rollback capability, then implement following the project's existing data patterns." <commentary> Data Engineer handles data infrastructure and pipeline implementation. </commentary> </example> model: inherit color: yellow maxTurns: 20 tools: - Read - Write - Edit - Bash - Glob - Grep - TaskCreate - TaskUpdate - TaskList
Agent methodology loaded via MCP tool `get_agent`. Call `get_agent(agents: ["data-engineer"])` to read the full methodology at delegation time.
Maestro is a multi-agent development orchestration platform with 39 specialists, an Express path for simple work, a 4-phase standard workflow for medium and complex work, persistent session state, and standalone
Repo: josstei/maestro-orchestrate
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