Data Code Review

Perform a comprehensive data review of: $ARGUMENTS

Framework

This review ensures Trusted and Timely data products using principles from:

• •DAMA DMBOK - Data Management Body of Knowledge
• •Data Mesh - Domain ownership, data products, interoperability
• •Data Governance for Everyone - Practical governance principles

The Four Pillars

Process

Step 1: Identify Scope

First, identify what code needs to be reviewed:

• •If $ARGUMENTS is a file or directory, review that directly
• •If $ARGUMENTS is empty or ".", review recent changes (git diff) or prompt for scope
• •Focus on SQL, Python, dbt models, pipeline definitions, schema files

Step 2: Run Parallel Reviews

Spawn 4 subagents in parallel to analyze different aspects:

1. •

   data-architecture - Schema design, domain boundaries, data contracts

   • •Polysemes and global identifiers
   • •Naming conventions and standards
   • •Breaking changes and versioning
2. •

   data-engineering - Code quality, logic correctness, performance

   • •Transformation testing and idempotency
   • •Query optimization and CDC
   • •Error handling and recovery
3. •

   data-quality - Trust, timeliness, documentation

   • •Freshness SLOs and monitoring
   • •Data validation and constraints
   • •Consumer documentation and discoverability
4. •

   data-governance - Compliance, lifecycle, ownership

   • •PII classification and masking
   • •Retention policies and backup
   • •Lineage and ownership clarity

Each agent should:

• •Read the base framework from .claude/prompts/data/_base.md
• •Read their specific checklist from .claude/prompts/data/[pillar].md
• •Review the code against their checklist
• •Return findings in standard table format

Step 3: Synthesize Results

After all agents complete:

1. •Collect findings from all 4 pillars
2. •Deduplicate — Some issues may be flagged by multiple reviewers
3. •Aggregate maturity assessments — Merge criteria assessments from all subagents into one maturity view
4. •Determine maturity status per level:
   • •All criteria ✅ → pass (✅)
   • •Mix of ✅ and ❌ → partial (⚠️)
   • •All criteria ❌ → fail (❌)
   • •Previous level not passed → locked (🔒)
5. •Prioritize findings by maturity level (HYG first), then severity (HIGH → LOW)

Output Format

# Data Review — Maturity Assessment

## Maturity Status

| Level | Status | Summary |
|-------|--------|---------|
| Hygiene | ✅/⚠️/❌ | [one-line summary] |
| Level 1 — Foundations | ✅/⚠️/❌/🔒 | [one-line summary] |
| Level 2 — Operational Maturity | ✅/⚠️/❌/🔒 | [one-line summary] |
| Level 3 — Excellence | ✅/⚠️/❌/🔒 | [one-line summary] |

**Immediate Action:** [Top hygiene failure if hygiene not passed, else top action from next achievable level]

---

## Hygiene

[If any failures: list them with severity, pillar, location, finding, recommendation]
[If all pass: ✅ All hygiene criteria met]

## [Next Achievable Level] — Detailed Assessment

For each criterion:
- ✅ **[Criterion]** — Evidence: `file:line` description
- ❌ **[Criterion]** — Missing: what should exist
- ⚠️ **[Criterion]** — Partial: what's there and what's missing

## Higher Levels — Preview

> **Level [N+1]**: [Brief list of criteria — not yet assessed in detail]
> **Level [N+2]**: [Brief list of criteria]

---

## Detailed Findings

| Priority | Severity | Maturity | Pillar | Location | Finding | Recommendation |
|----------|----------|----------|--------|----------|---------|----------------|

## What's Good

[Positive data patterns observed — well-designed schemas, good testing, clear documentation, proper governance]

Relationship to Other Reviews

Overlaps are intentional - each review applies its own lens to shared concerns.