---
name: data-pipeline-code-review-checker
description: "Reviews SQL, dbt models, or Python pipeline code across 9 dimensions — correctness, idempotency, testability, performance, data quality, error handling, naming/structure, dependency safety, and documentation — and returns line-level findings ranked by severity plus a prioritized fix list. Use when reviewing a pull request for a data pipeline, checking a dbt model or SQL transformation before merge, or auditing Python ETL/ELT code for reliability and best practices."
argument-hint: "[code-type] [code]"
allowed-tools: ["Read"]
---

# Data Pipeline Code Review Checker

## Purpose
Review data pipeline code — SQL, dbt models, or Python ETL/ELT — for correctness, reliability, testability, and maintainability, returning severity-ranked findings with concrete fixes so a reviewer can approve, request changes, or refactor with confidence. Catches the failure modes that only surface in production: silent data loss, non-idempotent reruns, fan-out joins, and untested assumptions.

## When to Use
Invoke this skill when the user asks to:
- Review a pull request or diff containing SQL, dbt, or Python pipeline code
- Check a dbt model before merging it into a project
- Audit a SQL transformation for correctness and performance
- Review Python ETL/ELT scripts for reliability and error handling
- Get a second opinion on whether pipeline code is production-ready
- Identify why a pipeline is flaky, slow, or producing wrong numbers

## Inputs Required
Before starting, collect:
- **Code type**: SQL / dbt model / Python (ETL, ELT, or orchestration)
- **The code**: The full file(s) or diff to review
- **Warehouse/engine**: Snowflake / BigQuery / Redshift / Postgres / Spark / DuckDB (affects performance and syntax guidance)
- **Materialization** (dbt): table / view / incremental / ephemeral — or "unknown"
- **Run frequency**: How often it runs (hourly, daily, on-demand) and whether it's backfilled
- **Upstream sources**: Where the data comes from and known volume/cardinality
- **Existing tests**: What tests or assertions already exist, if any

If warehouse, materialization, or frequency are missing, assume a daily-scheduled Snowflake incremental model and flag the assumption in the output.

## Instructions

Review the code across all 9 dimensions below. For each finding, assign a severity, cite the specific line or block, explain the failure it causes, and give a concrete fix (rewritten snippet where practical).

**Severity scale**:
- **Blocker** — will produce wrong data, lose rows, or fail in production
- **Major** — reliability, performance, or maintainability risk that should be fixed before merge
- **Minor** — style, naming, or improvement that can follow up

### Dimension 1 — Correctness & Logic
- **Join fan-out**: Any join on a non-unique key that silently multiplies rows — check that join keys are unique on at least one side, or that the many-to-many is intentional
- **Null-handling in filters/joins**: `WHERE col != x` silently drops nulls; `=` joins skip null keys — flag both
- **Aggregation grain**: Does the `GROUP BY` grain match what downstream consumers expect? Flag mixed grain (aggregate + non-aggregate columns without grouping)
- **Window frame defaults**: Implicit `RANGE` vs `ROWS` frames producing unexpected results with ties
- **Type coercion**: Implicit casts (string → date, int → float) that can silently corrupt or error

### Dimension 2 — Idempotency & Reruns
- Can this run twice and produce the same result? Flag `INSERT` without dedup, `CREATE TABLE` without `OR REPLACE`, appends without a delete-first strategy
- **Incremental logic** (dbt): Does the `is_incremental()` block use a watermark that handles late-arriving data? Flag `>` when `>=` plus dedup is safer
- **Non-deterministic sources**: `CURRENT_TIMESTAMP`, `RANDOM()`, or unordered `LIMIT` that make reruns non-reproducible
- **Backfill safety**: Will a full-refresh or backfill double-count or clash with the incremental path?

### Dimension 3 — Testability
- Are there tests for the assumptions this code depends on? Flag missing `unique` + `not_null` tests on primary keys (dbt) or missing assertions (Python)
- **Referential integrity**: Foreign keys without `relationships` tests
- **Accepted values / ranges**: Enum columns and numeric bounds without tests
- **Row-count / freshness expectations**: No guard against silent zero-row output or stale source data
- For Python: is business logic in pure, unit-testable functions or tangled with I/O?

### Dimension 4 — Performance
- **Full scans on partitioned/clustered tables**: Filters that don't hit the partition/cluster key on BigQuery/Snowflake/Redshift
- **`SELECT *`** in models feeding downstream tables — bloats storage and breaks on schema drift
- **Subquery vs CTE vs window**: Repeated subqueries that should be CTEs; correlated subqueries that should be joins or windows
- **Data-type-driven cost** (BigQuery bytes scanned, Redshift dist/sort keys, Snowflake pruning) — flag anti-patterns for the stated engine
- **Python**: row-by-row loops over DataFrames, unbatched API/DB calls, loading full tables into memory when streaming/chunking is possible

### Dimension 5 — Data Quality Guards
- Deduplication strategy present and correct where the source can contain duplicates
- Explicit handling of nulls, empty strings, and sentinel values ("N/A", 0, 1900-01-01)
- Currency/unit/timezone consistency — flag naive datetimes and mixed timezones
- Schema-drift resilience: does the code assume columns that may disappear or rename?

### Dimension 6 — Error Handling & Observability (Python-heavy, SQL where relevant)
- Bare `except:` or swallowed exceptions that hide failures
- No retry/backoff on transient network or warehouse errors; no idempotent retry
- Silent partial failures — a batch fails mid-loop and the job still reports success
- Missing logging of row counts, run duration, and key decision points
- No alerting hook or dead-letter path for bad records

### Dimension 7 — Naming, Structure & Readability
- CTE and column names that describe intent, not mechanics (`active_customers`, not `t2`)
- Model/file naming follows layer conventions (staging → intermediate → marts; `stg_`, `int_`, `fct_`, `dim_`)
- Single-responsibility: one model/function does one thing; flag 300-line monolith CTEs
- Consistent casing, aliasing, and formatting

### Dimension 8 — Dependency & Lineage Safety
- **dbt**: `ref()` / `source()` used instead of hardcoded table names (breaks lineage and environments)
- Hardcoded database/schema/project names instead of env-aware config
- Circular or hidden dependencies; undeclared upstream reads
- Secrets, connection strings, or credentials in code (Blocker on sight)

### Dimension 9 — Documentation & Contracts
- Model/column descriptions present for anything consumed downstream (dbt `.yml`)
- Non-obvious business logic explained inline (why, not what)
- Assumptions about grain, freshness, and source guarantees documented
- Contract/schema declared where downstream systems depend on stability

### Final Step — Synthesize
1. Count findings by severity
2. Produce a **prioritized fix list** — Blockers first, then Majors by effort-to-impact, then Minors
3. Give a one-line **verdict**: Approve / Approve with comments / Request changes / Needs refactor — and state the single most important thing to fix first

## Output Format
- **Structure**: Verdict banner → summary table of findings by severity → dimension-by-dimension findings (only dimensions with findings) → prioritized fix list
- **Length**: 700–1,300 words depending on code size
- **Format**: Structured document — a severity summary table, then findings as grouped bullets with line references and code snippets, then a numbered fix list
- **Sections**: Verdict → Findings Summary (table: Severity | Count) → Findings by Dimension → Prioritized Fix List → Most-Important-First recommendation
- **Tone**: Senior data engineer in code review — direct, specific, evidence-based; every finding cites a line and gives a fix, never vague praise or vague criticism

## Quality Checklist
Before delivering output, verify:
- [ ] Every finding cites a specific line, block, or CTE — no "somewhere in the file"
- [ ] Every Blocker and Major includes a concrete fix or rewritten snippet
- [ ] Findings are labeled with severity from the defined scale (Blocker / Major / Minor)
- [ ] The findings summary table's counts match the number of findings listed
- [ ] Join fan-out and idempotency are explicitly checked, even if the verdict is "no issue found"
- [ ] A single verdict is stated and the top-priority fix is named — no hedging

## Notes
- The two findings that most often reach production undetected are join fan-out (silent row multiplication) and non-idempotent reruns (double-counting on backfill) — always report on both explicitly, even to confirm they're clean.
- Tailor performance guidance to the stated engine: partition pruning matters most on BigQuery, dist/sort keys on Redshift, clustering and micro-partition pruning on Snowflake.
- For Python, favor separating pure transformation logic from I/O — this single change resolves most testability and error-handling findings at once.
- If the code is a diff rather than a full file, review the changed lines but flag when a change depends on unseen upstream context that should be verified.

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