data-quality-auditor

Testing & Quality

Audit data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.

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Source SKILL.md: https://github.com/borghei/Claude-Skills/blob/HEAD/engineering/data-quality-auditor/SKILL.md

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Data Quality Auditor

End-to-end data quality (DQ) practice: define DQ dimensions, write rule-based checks, detect schema drift, monitor freshness SLAs, respond to DQ incidents, build a maturity-graded program. Tool-agnostic — works whether you use Great Expectations, dbt tests, Soda Core, Monte Carlo, custom SQL, or hand-rolled scripts.

This skill is audit-focused, not pipeline-focused. For pipeline design, ETL, Spark/dbt, see engineering/senior-data-engineer.

Core Capabilities

Six DQ dimensions — completeness, accuracy, consistency, timeliness/freshness, validity, uniqueness (plus integrity, conformity, reasonableness); at least one check per dimension at production stage.
DQ check catalog — five categories (volume, freshness, schema, values, distribution) of ~50 specific check patterns applied per dataset.
Schema drift detection — snapshot baseline schemas and diff added/removed/changed columns, types, and ordinals with severity.
Freshness SLA monitoring — per-table max-age budgets with alerting-ready output.
Incident response — severity classification (Sev1-4) and a 7-step playbook (acknowledge → quarantine → triage → contain → fix-forward → notify → post-incident) with recovery patterns.
Maturity & governance — a five-level maturity model and anti-pattern catalog to grade and improve a DQ program.

When to Use

Situation	Skill applies
Setting up DQ from scratch on a new pipeline	Yes — start with DQ dimensions + check catalog
Auditing existing pipelines for missing DQ	Yes — `dq_check_runner.py`
Detecting schema drift in upstream sources	Yes — `schema_drift_detector.py`
Monitoring freshness / SLA on data assets	Yes — `freshness_monitor.py`
Responding to a DQ incident (bad data in prod)	Yes — incident response playbook
Designing a DQ governance model	Yes — DQ maturity model
Compliance evidence (SOC 2 PI1, GDPR, ISO 27001)	Yes — checks produce auditable artifacts
Building data pipelines for the first time	Use `engineering/senior-data-engineer` first

Clarify First

Before running the audit, confirm these inputs. If any is unknown or vague, ASK — do not assume:

Dataset & target — which table, pipeline, or store to audit (the data the scripts read via --data)
Which check — DQ rule checks, schema drift, or freshness SLA (selects dq_check_runner.py vs schema_drift_detector.py vs freshness_monitor.py)
Thresholds & SLAs — per-dimension pass thresholds and the freshness max-age budget (sets the pass/fail line and --max-age-min)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

The six DQ dimensions

Industry-standard taxonomy. Every dataset should have at least one check per dimension when at production stage.

Dimension	Question	Example check
Completeness	Are required fields populated?	`users.email IS NOT NULL` — fail if > 0.1% nulls
Accuracy	Do values match reality?	Reconciliation against source-of-truth system; sample-based human review
Consistency	Do values agree across systems / time?	`users.email` in DB matches Salesforce; row count today within 5% of yesterday
Timeliness / Freshness	Is data current to expectation?	`events_table.max(event_time)` is < 1h old; pipeline runs SLA
Validity	Do values conform to format / schema / business rules?	Email regex matches; country code in ISO 3166-1; status in known enum
Uniqueness	Are entities not duplicated?	`users.user_id` is unique; no two rows with same `(user_id, day)`

Some teams add: Integrity (referential — FKs resolve), Conformity (matches a published standard), Reasonableness (passes basic sanity checks beyond strict validity).

The DQ check catalog

Checks group into five categories applied per dataset — Volume, Freshness, Schema, Values, and Distribution. See the category summary and the full ~50-pattern catalog in references/dq-check-catalog.md.

Tools

Tool	Purpose	Command
`dq_check_runner.py`	Run/profile DQ checks against tabular data; per-table pass/fail/warning with value vs threshold	`python scripts/dq_check_runner.py --data t.json --checks checks.json --format json`
`schema_drift_detector.py`	Diff a current schema against a baseline snapshot (added/removed/changed columns, types, ordinals)	`python scripts/schema_drift_detector.py --baseline base.json --current cur.json`
`freshness_monitor.py`	Check a freshness SLA: current age vs max-age budget, alerting-ready output	`python scripts/freshness_monitor.py --data t.json --column updated_at --max-age-min 60`

All scripts: stdlib only, argparse CLI, JSON or human-readable output (see Scope re: live DB integration).

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

references/data-quality-dimensions.md — the 6 dimensions in depth: how to measure, threshold guidance, alerting strategy, and common pitfalls per dimension. Read when defining checks for a specific dimension.
references/dq-check-catalog.md — the full catalog of ~50 specific check patterns with detection heuristics and tool snippets (Great Expectations / dbt / Soda / SQL). Read when writing concrete checks.
references/dq-incident-response.md — the full incident playbook: severity classification, the 7-step response loop, recovery patterns (backfill, quarantine, DLQ, idempotent reprocessing, rollback), and post-incident writeup/notification templates. Read when responding to a DQ incident.
references/dq-maturity-model.md — the five-level DQ maturity model (L0 reactive → L4 data-as-product) and what to invest in at each level. Read when grading or planning a DQ program.
references/dq-workflows.md — the five end-to-end workflows (new dataset, schema drift, freshness SLA, auditing existing pipelines, post-incident improvement) with the exact script commands. Read when executing a concrete DQ task.
references/dq-anti-patterns.md — the catalog of DQ anti-patterns to avoid. Read when reviewing an existing DQ program for smells.

Scope & Limitations

This skill covers:

Auditing data quality across pipelines, warehouses, and stores against the six DQ dimensions.
Rule-based check design, schema drift detection, and freshness SLA monitoring (tool-agnostic).
DQ incident response and a maturity-graded governance program.
Producing auditable DQ artifacts for compliance evidence (SOC 2 PI1, GDPR accuracy, ISO 27001).

This skill does NOT cover:

Pipeline design, ETL, dbt/Spark implementation — use engineering/senior-data-engineer.
Live database querying; scripts are stdlib-only and read JSON inputs or simulate. For production, integrate your DB driver of choice (psycopg / mysqlclient / google-cloud-bigquery / etc.).

Related skills

engineering/senior-data-engineer — pipeline design, ETL, dbt, Spark
engineering/observability-designer — observability for data infrastructure (adjacent to DQ)
engineering/chaos-engineering — DQ checks benefit from chaos testing
ra-qm-team/gdpr-dsgvo-expert — DQ underpins GDPR Art. 5(1)(d) "accuracy"
ra-qm-team/soc2-compliance-expert — SOC 2 PI1 (Processing Integrity) requires DQ controls