Project SOW
Validation Pipeline Build
Data quality engineering · automated validation · GS1/GTIN compliance checks
The audit found $93,000 a year in data-traced chargebacks — 64% of the $144.7K annual total — and traced every one to the field that caused it. Twelve months later, the same defects are back — because the audit fixed the data, and nothing fixed the flow.
An audit is a snapshot; data defects are a flow. They enter through manual entry, partner files, broker submissions, and system handoffs — and unless something checks the data beforeit lands, this year’s findings become next year’s findings with new dates. The fix is a validation layer: automated checks that run where the defects enter, with a defined path from every flag to its resolution.
This engagement is the natural second purchase. The audit tells you what’s broken and what it costs; the pipeline is what keeps it fixed.
The standards the pipeline enforces — GS1/GTIN anatomy, GDSN attribute requirements, retailer item-setup rules, and logistics dimensions — are covered in the Data Standards Cheat Sheet.
What gets built
Inbound validation that runs before data enters your systems: format and type checks, phantom-duplicate detection, GS1/GTIN validation, dimension-and-weight integrity, and retailer-specific rules for the partners you ship to. Item-setup preflight — new-item forms checked against the retailer’s schema before submission, not after rejection. And an exception-handling protocol: every caught defect gets an owner, a path, and a resolution state, so quality stops depending on who happens to notice.
The pipeline is documented and handed off. It runs without me — that’s the point.
See it worked through
The architecture is public, end to end:
Cinderhaven data platform
A modern source-to-mart data platform for CPG data shapes: pipelines, data quality testing, orchestration, and lineage. Python, Postgres, dbt, Dagster.
github.com/lailarallc/cinderhaven-data-platform →Item-setup preflight
Codified partner schemas and a typed validation engine that flags new-item form rejection risk before submission.
github.com/lailarallc/item-setup-form-preflight →Dimension & weight integrity
Validation for the dim-weight defects behind freight chargebacks and compliance fines.
github.com/lailarallc/dimension-weight-integrity →What you get
A defined SOW: scope, timeline, fixed deliverables, clear finish line. Typically 4–8 weeks depending on system count. The deliverable is a running pipeline, its documentation, and a team that knows how to operate it.
Next step — Data problems that keep coming back
Get the number for your own data. Seven business days.
The worked example above is synthetic so the method can be shown in full. The offers below run it on your data — the Snapshot credits in full toward the audit.
Private, expiring upload — never email. Mutual NDA before anything moves. Files destroyed within 30 days of delivery, with a certificate. Methods published, tools open source.
Start in writing.
Bring your audit findings — mine or anyone’s — to a short written intake. The pipeline scope falls out of the findings list. No deck, no obligation.