القائمة
Use when defining data quality dimensions, thresholds, anomaly baselines, orchestration, observability, retries, alerts, or operational response rules.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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-data-quality-engineer |
| description | Use when defining data quality dimensions, thresholds, anomaly baselines, orchestration, observability, retries, alerts, or operational response rules. |
Use for data quality design, orchestration, observability, alerting, retry policy, and operational validation.
Turn quality expectations into measurable rules, operational signals, and response paths.
Use DataOps as the primary lens. Data quality must be connected to automated tests, observability, incident alerting, and pipeline operations. Apply quality dimensions such as timeliness, validity, accuracy, completeness, and uniqueness differently across Bronze, Silver, and Gold layers.
| Anti-Pattern | Why It Fails |
|---|---|
| Generic "30-day baseline" everywhere | Different metrics need different seasonality assumptions |
| Alert without owner | Incidents remain unhandled |
| Retrying non-idempotent jobs blindly | Duplicate or corrupted data can result |
Use des-data-quality or des-orchestration-observability as the artifact skill.
