Data Quality Checks

Design the data quality checks for a table or pipeline across the standard dimensions. Use when asked to add data quality tests, define DQ checks, catch bad data before it hits dashboards, or set up monitoring for a dataset. Produces a checks plan across completeness, validity, uniqueness, freshness, consistency, and accuracy — each with the rule, severity, and where it runs (dbt test / Great Expectations / SQL assertion).

Data Quality Checks Skill

Bad data quietly poisons dashboards and models until someone notices the number is wrong. The fix is checks that fail loudly before that — across the standard DQ dimensions. This skill designs them for a specific table/pipeline: the exact rule per dimension, its severity (block vs. warn), and where it runs (dbt test, Great Expectations, or a SQL assertion), so quality is enforced, not hoped for.

Required Inputs

Ask for these only if they aren't already provided:

• The table/pipeline and what it represents (grain, key columns).
• The columns that matter — keys, required fields, enums, ranges, dates.
• Freshness expectation — how current the data must be.
• Tooling — dbt tests, Great Expectations, Soda, or raw SQL assertions.

Output Format

Data Quality Checks: [table]

Checks organised by dimension — each with the rule, severity (🔴 block the pipeline / 🟡 warn), and where it runs:

Dimension	Check	Rule	Severity	Implement as
Completeness	required fields non-null	not_null on [cols]	🔴	dbt test
Uniqueness	grain key unique	unique on [key]	🔴	dbt test
Validity	values in allowed set/range	accepted_values / range	🟡	GE / SQL
Freshness	data is current	max(loaded_at) within SLA	🔴	dbt source freshness
Consistency	cross-field / cross-table	e.g. totals reconcile, FK exists	🟡	SQL assertion
Accuracy	matches a source of truth	reconcile vs. system-of-record	🟡	SQL assertion

Notes:

• Severity discipline — only block on checks that should stop the pipeline (a duplicated grain key, stale critical data). Over-blocking trains people to ignore alerts.
• Where to check — at ingestion (catch early) vs. in the model vs. post-build; recommend per check.
• On failure — what happens (halt, quarantine rows, alert + continue) and who's paged.

Quality Checks

• Covers the core dimensions (completeness, uniqueness, validity, freshness, consistency)
• Each check has an explicit rule and a severity (block vs. warn)
• Severity is disciplined — only truly critical checks block the pipeline
• Freshness has a measurable SLA, not "should be recent"
• Each check names where it runs and what happens on failure

Anti-Patterns

• Do not block the pipeline on every check — alert fatigue makes people ignore the real failures; reserve 🔴 for critical
• Do not only test the happy path — the grain key, nulls, and freshness are where the real breakage hides
• Do not write checks with no failure action — a test that fails into the void changes nothing
• Do not skip freshness — stale data that looks fine is the most dangerous kind
• Do not check only one table in isolation — cross-table consistency (FKs, reconciliations) catches integration bugs

Based On

Data-quality practice — the six DQ dimensions, dbt tests / Great Expectations / source-freshness, severity-tiered enforcement.