I'm the Principal Consultant at Lailara LLC — data hygiene and analytics for specialty food and CPG brands scaling into national retail. I find the money leaking through product data, deductions, and trade spend, and tell you exactly which field it's leaking from.
I'm not a software engineer by training. Twenty-five years of operations and incentive-fulfillment work taught me to build tools when the thing I need doesn't exist — and to publish them so other people can use them too.
- data-hygiene-auditor — A linter for your
data. Audits Excel/CSV files for mixed formats, misused fields, placeholder floods, and phantom
duplicates; outputs HTML, Excel, and PDF reports.
pip install data-hygiene-auditor· PyPI - datascope — Profiles every column in a workbook and
scores it across five data-quality dimensions, with strict cell-level type detection that catches
what pandas silently coerces.
pip install datascope-dq· PyPI
- GTIN Validator — product data validated against GS1 standards with retailer-specific context; branded PDF report with a fix roadmap
- EDI Preflight — checks 850s and 856s against retailer specs (Walmart, Amazon, UNFI, KeHE, and more) before you submit
- Retail Readiness Scorecard — eight-dimension readiness self-assessment for a retailer launch, ten minutes, PDF readout
- Cost of Saying Yes — month-by-month cash model for a major retailer launch, because revenue projections hide the trough
Full analytical engagements on a synthetic $25M specialty food brand — the data is invented so the methodology can be shown end to end, and the dollar figures are real outputs of the pipelines. Highlights:
- product-data-health-audit — every chargeback traced to the product-master field that caused it ($458K/yr quantified)
- retailer-deduction-recovery — 15,900 deductions traced through five compounding failures; recovery simulation 16% → 65%
- cinderhaven-data-platform — the data platform underneath it all: source-to-mart pipelines, quality testing, orchestration, lineage (Python · Postgres · dbt · Dagster)
The rest — trade spend forensics, OTIF reconciliation, SKU scoring, channel profitability, demand planning, shelf intelligence — lives at lailarallc.com/work.
- item-setup-form-preflight — typed validation against codified retailer schemas; catches new-item rejections before submission
- dimension-weight-integrity — dim-weight validation for the defects behind freight chargebacks
- internal-data-anonymizer — guided column-by-column anonymization with deterministic, format-preserving mappings
- data-differences-tool — diff two tabular files: rows added, removed, modified, with before/after values
Solo developer, Claude Code as pair programmer, phase-gated workflow with commit gates — packaged at claude-solo-dev-workflow.
Stack: Python · R · SQL · Quarto · dbt · Postgres · React · Power BI · Excel (the serious kind)
📍 Kentucky · lailarallc.com · LinkedIn