Data Quality Test Framework Builder
Generate Data Quality Tests & Anomaly Detection for dbt
What You Can Do
You'll build comprehensive data quality test suites that validate transformations, detect anomalies, and document standards across your dbt and SQL pipelines. This skill generates production-ready test configurations, anomaly detection protocols, and validation rules that catch data issues before they impact analytics.
Features
Automatically create YAML test configurations for freshness, completeness, uniqueness, and relationships
Build statistical methods to flag outliers, value distributions, and unexpected data patterns
Generate parameterized SQL queries that validate data ranges, constraints, and business logic
Create schedules and thresholds to monitor when tables were last updated and alert on staleness
Auto-generate test descriptions, ownership, SLAs, and remediation playbooks for your data quality standards
Identify which tables and columns lack tests and prioritize high-impact validation rules
Generate tests for dbt Cloud, Snowflake, BigQuery, PostgreSQL, and other SQL platforms
Build notification configurations that route data quality incidents to the right team members
Example Output
Example 1: dbt Test Suite Configuration
version: 2
models:
- name: customers
columns:
- name: customer_id
tests:
- unique
- not_null
- name: email
tests:
- unique
- matches_pattern:
pattern: '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'
tests:
- dbt_utils.recency:
datepart: day
interval: 1
field_name: updated_at
Example 2: Anomaly Detection Query
WITH stats AS (
SELECT
AVG(order_value) as mean_value,
STDDEV_POP(order_value) as std_dev
FROM orders
WHERE DATE(created_at) >= CURRENT_DATE - INTERVAL 30 DAY
)
SELECT
order_id,
order_value,
ROUND((order_value - stats.mean_value) / stats.std_dev, 2) as z_score
FROM orders, stats
WHERE DATE(created_at) = CURRENT_DATE
AND ABS((order_value - stats.mean_value) / stats.std_dev) > 3
Example 3: Test Report
✓ Completeness: 99.8% of required fields populated
✓ Freshness: Last update 2 hours ago (within 4-hour SLA)
✗ Anomalies: 12 outlier orders detected (values > 3σ)
⚠ Coverage: 18/22 columns tested (82%)
What's Included
- SKILL.md: Full framework for building data quality tests
- dbt test templates: Pre-built test patterns for common validation scenarios
- SQL validation library: Reusable queries for constraint checks, distribution analysis, and freshness monitoring
- Anomaly detection playbook: Statistical methods (z-score, IQR, seasonal decomposition) with implementation examples
- Test documentation generator: Template for auto-generating test ownership, SLAs, and remediation runbooks
- Multi-warehouse guide: Syntax and best practices for Snowflake, BigQuery, PostgreSQL, Redshift, and Databricks
Who It's For
- Data engineers — Build robust data quality frameworks for ETL/ELT pipelines and dbt projects
- Analytics engineers — Validate transformations and ensure data reliability for downstream analytics
- QA engineers — Create automated data validation suites that test data pipelines like you'd test code
- Data analysts — Document and enforce data quality standards to reduce data quality incidents
- Analytics platform teams — Implement organization-wide data quality governance and monitoring
Best For
- Setting up production dbt test suites from scratch
- Building anomaly detection for high-volume transaction tables
- Automating data validation across multiple warehouses
- Creating SLA-driven data quality dashboards and alerts
- Documenting and scaling data quality standards across teams
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