The Ultimate Code Review Checklist for Data Validation Frameworks

A comprehensive, production-ready checklist for reviewing data validation, ETL testing, and automated reconciliation codebases. Code reviews for data engineering tools need more rigor than standard web apps. A subtle bug in a data validation framework can cause silent pipeline failures, false positive test passes, or accidental execution of unbounded SQL queries on production warehouses. Whether you are building a custom data framework or maintaining automated ETL tests, use this generalized checklist during code reviews to keep your test suites secure, performant, and reliable. 1. Test Case Configuration (YAML / JSON) [ ] TC ID Matching: Ensure the tc_id value matches the configuration filename exactly. [ ] Schema Validity: Verify that type (e.g., count, data, recon, file) and source/target drivers are valid and supported. [ ] Explicit Enablers: Confirm the enabled field is explicitly set (true or false) rather than omitted. [ ] Relative File Paths: For file-based validation, ensure paths are relative to defined source/target data directories. [ ] Non-Empty Queries: Confirm SQL sources and targets include non-empty query strings or valid template paths. [ ] Unique Case IDs: Ensure test case identifiers are unique across the test suite directory. [ ] Documented Rationale: If a test case has enabled: false or uses numeric tolerance thresholds (validation_tolerance), ensure a comment explains the business reason. [ ] Dependency Order: Verify that basic structural checks (COUNT) run prior to deep comparisons (DATA / RECON). 2. SQL & Query Logic [ ] Explicit Projections: No SELECT *. All columns must be explicitly listed to avoid schema drift breaks. [ ] Alignment: Source and target queries must return compatible data types and matching column ordering. [ ] Environment Isolation: Check that query strings contain zero hardcoded hostnames, schema names, or environment paths. [ ] Secret Hygiene: Ensure queries contain no hardcoded credentials or connection strings. [ ] Warehouse Pushdown: Confirm filtering and heavy aggregation occur at the database level (WHERE / GROUP BY) rather than pulling full tables into application memory. [ ] Determinism: Queries must return deterministic ordering (e.g., explicit ORDER BY on primary keys) so differential comparisons yield consistent results. 3. Validator Core Logic [ ] Standardized Return Schema: Validator routines must always return a consistent payload schema (e.g., status, summary, src_row_count, tgt_row_count, matched_rows). [ ] Status Consistency: Status values should use normalized uppercase strings ("PASS" / "FAIL"). [ ] Null/NaN Handling: Verify that NaN and NULL comparisons explicitly account for missing data parity. [ ] Memory Guards: Row capping (e.g., .head(1000)) or chunking should be enforced to prevent OOM errors on large datasets. [ ] Safe Numbers: Ensure tolerance thresholds enforce non-negative values (e.g., max(0.0, float(tolerance))). [ ] Error Preservation: No silent exception swallowing — all except blocks must either re-raise or log through the logger framework. 4. Execution & Pipeline Runners Type Dispatcher Safety: Ensure the execution runner raises an explicit ValueError when encountering an unsupported validation type. Type Coercion Guards: Data loaders (e.g., CSV readers) should default string types (dtype=str) where appropriate to prevent silent type conversions (e.g., dropping leading zeroes in zip codes). Isolation: Ensure individual test execution is wrapped in try/except blocks so a single failing test case doesn't collapse the entire run. Output Capture: Redirect standard output streams properly so module logs are correctly captured in final HTML or JSON reports. 5. Security & Data Safety No Hardcoded Credentials: Check that API keys, passwords, or connection strings are strictly retrieved from environment variables or secret managers. No Raw PII: Confirm that local test fixtures (data/src, data/tgt) contain synthetic data instead of production PII or financial records. Injection Prevention: SQL execution calls must use parameterized inputs instead of raw string formatting (f-strings). Path Traversal Protection: Relative file operations must be validated to prevent directory traversal breakouts. 6. Code Quality & Maintenance Logging: Ensure all operational feedback uses structured logging frameworks instead of raw print() statements. Type Annotations: Function signatures must include type hints and clear docstrings. Ignore Management: Verify that dynamic run artifacts (.pyc, pycache, local HTML reports, local .log files) are properly tracked in .gitignore. Sign-Off Checklist Template When reviewing a PR, drop this checklist template into your code review comment: PR Code Review Sign-Off - Config & SQL Review Passed - Validator & Runner Logic Passed - Security & Secret Scanning Passed - Test Coverage Verified How do you handle data testing in your pipelines? Let me know in the comments!