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Engineering: backend-engineering, frontend-engineering, data-engineering, ml-engineering, platform-engineering, qa-methodology Executive: go-to-market, legal-strategy, operational-design, org-design, product-strategy ml-engineering: added missing training-infrastructure.md reference qa-methodology: added test-data-management, performance-testing, security-testing references All frontmatter converted to agent-skills convention. Source: https://github.com/magnus919/hermes-profiles
27 lines
1.2 KiB
Markdown
27 lines
1.2 KiB
Markdown
# Data Engineering
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Data engineering methodology — database operations (vector, relational, graph, time-series), ETL/ELT pipeline design (dbt patterns, incremental loading), SQL analytical patterns, data quality monitoring, schema migration, and storage infrastructure management. Grounded in operational patterns for production data systems.
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## Why Install This Skill
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Your agent gets operational patterns for production data systems — real SQL, dbt models, backup commands, and migration strategies instead of textbook theory.
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## What You Get
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| Directory | Purpose |
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| `SKILL.md` | Core methodology, trigger conditions, reference index |
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| `references/` | Deep-dive reference files loaded on demand |
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## Triggers
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Designing ETL/ELT pipelines, writing analytical SQL, operating vector/graph/time-series databases, planning migrations, or setting up data quality monitoring.
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## Requirements
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Platform-agnostic. References cover PostgreSQL, DuckDB, ClickHouse, BigQuery, Snowflake, Neo4j, InfluxDB, TimescaleDB, and dbt.
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## Quick Start
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Load SKILL.md for the methodology overview and reference table, then load specific references as needed for the task at hand.
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