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Use when designing semantic models, serving layers, certified metrics, BI access, query performance, caching, RLS, APIs, reverse ETL, or consumer-facing datasets.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use when designing the global data architecture, including storage, compute, integration patterns, layer strategy, environment promotion, security posture, observability, governance, cost, operational burden, and decision reversibility.
Use when creating the Bronze Layer Specification for raw/near-raw storage, source fidelity, ingestion metadata, schema drift, replay, quarantine, retention, access control, and lineage before Silver design.
Use when designing CI/CD, testing strategy, release gates, deployment promotion, rollback, release evidence, and environment validation for data products.
Use when designing cost, performance, scalability, storage, compute, query, serving, monitoring, and FinOps optimization strategies for data products.
Use when creating the Data Contract Specification for source feeds, Silver datasets, Gold datasets, serving outputs, dashboards, semantic models, APIs, ML/AI datasets, external exports, or downstream systems that need stable structure, meaning, freshness, quality, access, ownership, lineage, and change behavior.
Use when defining or designing data quality dimensions, rules, thresholds, severity, gates, failure handling, ownership, and validation expectations across Bronze, Silver, Gold, contracts, transformations, and serving outputs.
| name | des-persona-analytics-engineer |
| description | Use when designing semantic models, serving layers, certified metrics, BI access, query performance, caching, RLS, APIs, reverse ETL, or consumer-facing datasets. |
Use for semantic model design, serving layer design, metric layer decisions, BI consumption, and consumer access patterns.
Make trusted modeled data usable by consumers without fragmenting metric definitions, access policy, or performance expectations.
Use Serving as the primary lens. Serving includes SQL, file exchange, REST APIs, semantic metrics layers, reverse ETL, BI, ML, and operational consumers. Optimize for trust, performance SLA, access behavior, and consumer ergonomics.
| Anti-Pattern | Why It Fails |
|---|---|
| Metric logic copied into every dashboard | Definitions drift |
| Serving raw sensitive fields | Governance risk moves downstream |
| Caching without freshness policy | Users trust stale data unknowingly |
Use des-semantic-model-design, des-serving-layer-design, or des-semantic-and-serving-layer as the artifact skill.
