Data engineering services · Atlanta · Since 2002

Numbers your team can trust, from every system you run.

A working data platform in weeks, for a fraction of a data-engineer hire, with one owner accountable after launch.

Fig. 01 · Live lineageHover, tap or tab to a system to trace it
ShopifyOrders · refundsStripeCharges · feesAd platformsMeta · GoogleCRMPipeline · dealsQuickBooksLedger · payroll3PLShip · pick · packLegacy ERPLanded costbudget.csvSomeone's desktopWarehouseYour cloudStaging✓Intermediate✓Marts✓Semantic layerdbt · tested · contractedTrue marginby SKUCAC paybackby channelCashnext 13 weeksShopifyOrders · refundsStripeCharges · feesAd platformsMeta · GoogleCRMPipeline · dealsQuickBooksLedger · payroll3PLShip · pick · packLegacy ERPLanded costbudget.csvSomeone's desktopWarehouseYour cloudStaging✓Intermediate✓Marts✓Semantic layerTrue marginby SKUCAC paybackby channelCashnext 13 weeks
Status8 sources · 1 warehouse · 3 answers
SourcesEvery one, legacy included
DeliveryLive in weeks
CostA fraction of a hire
After launchOne owner, accountable
§ 02Track Record

Building production data systems since 2002, at CNN, Whatnot, Cars.com and Randstad. Today we also run the production operations software and data platforms behind two national franchise systems, used daily by more than 500 operators across the US and Canada.

§ 03The true cost of a sale

What did that order actually earn?

Every system you run knows one slice of a sale. Take one order from an online store: the storefront knows the price, Stripe knows the fee, the 3PL knows the shipping, the ad platforms know what the click cost, payroll knows what it cost to pick and pack, and the ERP knows what the product cost to land. Joined on the order, they give you the margin you actually made, and the same join works for a job, a contract or a franchise location.

Storefront gross margin64.6%
Fully loaded contribution20.3%

Illustrative order. Six systems, one number each, joined on the order ID.

Fig. 03 · Exploded viewOrder #1047 · 1 × walnut desk organizer
Exploded view of one illustrative $148.00 order. Cost of goods $52.40 from the legacy ERP, shipping $14.85 from the 3PL invoice, pick and pack labor $6.10 from QuickBooks payroll, ad spend $31.20 from Meta and Google, payment fees $4.59 from Stripe, returns reserve $8.88 from Shopify. Contribution left: $29.98, or 20.3 percent.Order #1047$148.00 · storefront says 64.6% marginCOGSLegacy ERP · Landed cost, freight in−$52.40Shipping3PL invoice · Zone 6, 2 lb−$14.85Pick + packQuickBooks · Payroll, allocated−$6.10Ad spendMeta + Google · Attributed, blended−$31.20Payment feesStripe · 2.9% + 30¢−$4.59Returns reserveShopify · 6% trailing rate−$8.88ContributionWhat is left · 20.3% of revenue$29.98Exploded view of one illustrative $148.00 order. Cost of goods $52.40 from the legacy ERP, shipping $14.85 from the 3PL invoice, pick and pack labor $6.10 from QuickBooks payroll, ad spend $31.20 from Meta and Google, payment fees $4.59 from Stripe, returns reserve $8.88 from Shopify. Contribution left: $29.98, or 20.3 percent.Order #1047$148.00 · 64.6% marginCOGS−$52.40 Legacy ERPShipping−$14.85 3PL invoicePick + pack−$6.10 QuickBooksAd spend−$31.20 Meta + GooglePayment fees−$4.59 StripeReturns reserve−$8.88 ShopifyContribution$29.98 What is left
§ 04Economics

A $150k problem, solved in weeks.

Joining every system you run used to mean hiring a senior data engineer, a $100k to $150k commitment that takes quarters before anyone trusts a number. We build the same platform for a fraction of that hire, live in weeks, and stay accountable for it after launch.

Fig. 04 · Cost and time to a working platformIllustrative · cost not to scale
Spec
Option AOne senior hire
Option BWide Open Tech
Cost
$100k to $150k or more a year in salary, before benefits, tools and recruiting fees.
A fraction of that, scoped and sized in the Blueprint before any build starts.
First answer
Months of recruiting and onboarding before the build starts.
A working platform in weeks, with real data flowing early.
Coverage
One person’s judgment, one person’s review, one person’s vacation.
Nothing depends on one person’s memory: every change gets an independent review and a person’s approval, and the platform is documented and tested in your repo.
Range
Strong in the stack they know, learning the rest on your time.
Any stack, any industry: warehouse, ingestion, models, tests and reporting, legacy systems with no API included.
If they leave
The knowledge walks out with them.
Code, tests and docs live in your repo and your cloud account, and one owner stays accountable after launch.
§ 05Trusted Answers

Every number, traced to where it came from.

Your weekly meeting stops being a debate about whose number is right, because every report reads the same tested definitions, and any number you doubt can be traced back through the checks that ran to the system it came from.

semantic-layer · ask warehouse online
ask ›
▸ metric   contribution_margin · v3 · owner: finance
▸ joins    shopify.orders · stripe.charges · meta.spend · google.spend · 3pl.invoices · erp.landed_cost
▸ checks   212 passed · 0 failed · fresh 14 min ago
▸ review   change reviewed by an independent agent · approved by a person
ChannelOrdersRevenueMarginCM %
Email0$0$00.0%
Google0$0$00.0%
Meta0$0$00.0%
Meta drives the most revenue at the thinnest margin, 27.0% once returns and fulfillment land. Email earns nearly twice as much per dollar of sales.
lineage ▸contribution_margin←fct_order_margin←int_order_costs←stg_3pl__invoicesstg_stripe__feesstg_erp__landed_cost
Demo data
  • Defined onceRevenue, margin, CAC, repeat rate: each metric has one definition in the semantic layer and one owner, so finance, marketing and operations read the same number.Console: contribution_margin · v3 · owner: finance
  • TestedTests and data contracts run on every model, every run. Each test is written and confirmed failing before the code it covers lands, so a passing test means something.Console: 212 passed · 0 failed
  • TraceableEvery answer shows where it came from, down to the source table. If you doubt a number, you can follow it home.Console: contribution_margin ← fct_order_margin ← int_order_costs ← stg_3pl__invoices
  • MonitoredFreshness and volume checks watch every source. When one changes shape or goes quiet, the pipeline stops and we hear about it before a wrong number reaches a dashboard.Console: fresh 14 min ago
  • ReviewedThe agent that checks a change is never the one that wrote it, and a person approves every plan and every merge.Console: reviewed by an independent agent · approved by a person
§ 06The Platform

What you actually get.

Five layers, built in your own cloud account on your stack, and you own every one of them. Each layer is built so the one above it can trust what it reads.

L1

Ingestion

Every system you run, including the legacy one with no API and the spreadsheet finance keeps by hand. Raw data lands first, untouched, so nothing is lost.

SourcesPOS · accounting · CRM · HR · ads
L2

Warehouse

Stood up fresh or connected to the one you already run. It lives in your own cloud account and you own it outright.

Runs onSnowflake · Databricks
L3

Models

Tested dbt models and a semantic layer, so a metric means the same thing in every report and every meeting.

Built withdbt · semantic layer
L4

Controls

Tests, contracts, lineage and monitoring written as we build. A broken source stops the pipeline before the dashboard finds out.

CoverageEvery model, every run
L5

Reporting

The reports your team actually opens on Monday, built on the same definitions as the rest of the platform, with one owner accountable after launch.

Delivered inPower BI · your BI tool
§ 07Ways To Start

Two ways to start.

Both are small, scoped first engagements that leave you with something useful whether or not we build the platform together.

.01
Data Platform Blueprint

We survey every system you run, map which questions each one can answer on its own and which need several joined, and hand you a sized, phased plan for the platform.

Small · every system, spreadsheets included

.02
Conversion Data Health Check

We check whether the revenue your ad platforms see matches your books, and show you where it breaks, why, and what a durable fix looks like.

Small · fixed scope · read-only

.03
The platform

Where both of them lead: the warehouse, ingestion, tested models and reporting from the plan, built in weeks, with one owner accountable after launch.

Sized in the Blueprint

§ 08Fully Integrated

Built to work with your stack.

We work on top of the systems you already run, in any industry. We connect Snowflake or Databricks in your own cloud account, or stand one up with dbt underneath, and your source systems stay exactly where they are.

Snowflake
Databricks
dbt
PostgreSQL
Python
TypeScript
Next.js
FastAPI
AWS
Azure
GCP
Pulumi
Terraform
Datadog
Sentry
Slack
Power BI
QuickBooks
HubSpot
Stripe
§ 09FAQ

Before you ask.

Next step

Start with a map of every system you run.

The Data Platform Blueprint surveys your systems, maps which questions each one can answer, and hands you a plan for the platform. Tell us what you run and we will reply within 24 hours.