analytics-engineering
Practice analytics engineering — reliable, tested, versioned data transformations that turn raw sources into trustworthy analytical tables. Use when building dbt-style models, curated marts, or the semantic layer between raw data and BI.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- analytics-engineering
- description
- Practice analytics engineering — reliable, tested, versioned data transformations that turn raw sources into trustworthy analytical tables. Use when building dbt-style models, curated marts, or the semantic layer between raw data and BI.
03 SKILL.md 正文
Analytics Engineering
Overview
Analytics engineering applies software engineering discipline to analytical data transformation: modular SQL/models, tests, documentation, and CI.
When to Use
- Building curated warehouse marts
- Replacing fragile spreadsheet pipelines
- Creating shared metrics tables for BI
- Scaling analyst work beyond one-off queries
Core Practices
- Model in layers (staging → intermediate → marts)
- Test assumptions (unique, not_null, relationships)
- Document models and columns for consumers
- Version control all transformations
- Use CI to catch breaking changes
- Define clear contracts with data producers and consumers
Principles
- One trusted mart beats ten conflicting extracts
- Tests encode business rules
- Analytics code deserves code review
- Change management protects downstream users
Verification
- [ ] Models are layered and tested
- [ ] Documentation is discoverable
- [ ] Changes go through version control / CI
04 同仓库更多 Skills
compliance-monitoring
Design compliance monitoring and testing programs that verify controls work in practice and surface issues before regulators do. Use when building monitoring plans, compliance testing, or continuous control monitoring.
complaints-handling
Operate complaints-handling processes that meet regulatory standards — logging, timely resolution, root-cause analysis, and fair outcomes. Use when designing or improving regulated complaints frameworks or analyzing complaint themes for conduct risk.
aml-kyc-basics
Apply AML/KYC fundamentals — customer due diligence, monitoring, suspicious activity escalation, and recordkeeping — for financial crime compliance. Use when designing onboarding controls, transaction monitoring concepts, or AML program components in financial services.
ai-model-compliance
Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.
data-quality-management
Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.
data-lineage
Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.