Engineering™ · Revenue & MarTech

Revenue Data Infrastructure

We connect your commercial and billing systems to build a single source of truth for your business.

Data engineering layer unifying CRM, billing, payment gateways, web analytics, and ad platforms into a governed cloud repository to compute CAC, LTV, and net profitability with mathematical certainty.

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Scope

Multi-System Data Pipeline · Data Cleaning & Modeling · Real-Time Revenue Sync · Single Customer View

Estimated Timeline

5–8 weeks

Platforms

BigQuery / Snowflake · dbt · Fivetran / Airbyte · Stripe / ERP APIs

Target Fit

Is this for your company?

This service is for you if

✓Different departments report conflicting revenue, customer count, and margin figures.
✓Your finance and growth teams spend weeks every month manually reconciling spreadsheets.
✓You cannot reliably calculate Customer Lifetime Value (LTV) by marketing channel or acquisition cohort.
✓You see major discrepancies between ad platform reported revenue and cash collected in your bank.

You probably do not need it if

✕You operate on a single simple eCommerce platform where all metrics are already centralized.
✕Your transaction volume is too low to justify maintaining a dedicated cloud data warehouse.

Problem Space

What we solve

Commercial Data Silo Elimination

Automated data pipelines connecting CRM, payment gateways (Stripe/PayPal), ERP, and ad platforms.

Mathematical CAC, LTV & Churn Modeling

Semantic layer reconciling paid ad spend with actual collected cash and gross profit margins.

Single Customer View Architecture

Unified customer identity tracking browsing behavior, commercial history, and support tickets.

Zero-Maintenance Automated ELT Pipelines

Continuous extraction, loading, and transformation (ELT) pipelines that update business data autonomously.

Engineering Process

How it works

01 — Diagnose

Data Source & Schema Audit

We map schemas across your CRM, ERP, core database, and billing gateways to pinpoint reconciliation gaps.

02 — Design

Revenue Dimensional Model Blueprint

We design a dimensional star schema defining canonical business metrics and customer entity identities.

03 — Build

Pipeline Ingestion & dbt Modeling

We configure automated connectors, write modular dbt transformation models, and implement data quality tests.

04 — Launch

Financial Reconciliation & BI Exposure

We reconcile figures against actual bank accounting ledgers and expose clean views to executive dashboards.

Deliverables

What we deliver

Upon completion you will have

✓Cloud Data Warehouse (BigQuery or Snowflake) structured with governed schemas.

✓Automated ELT pipelines running on scheduled or near-real-time synchronization.

✓Version-controlled dbt models with automated schema and data integrity tests.

✓Unified semantic data model for customers, transactions, cohorts, and acquisition costs.

✓Comprehensive data dictionary and financial metric specification documentation.

Delivery Plan

Implementation Phases

Tiempo típico de proyecto:5–8 weeks

Weeks 1–2Phase 1

Data Mapping & Metric Definitions

API discovery across billing, CRM, and ad platforms; formalizing mathematical CAC and LTV formulas.

Weeks 3–5Phase 2

Warehouse Setup & Ingestion Pipeline

Cloud data warehouse provisioning, automated pipeline deployment, and baseline data loading.

Weeks 6–7Phase 3

Transformation & dbt Semantic Modeling

Data cleansing, entity deduplication, and dimensional analytics table construction.

Week 8Phase 4

Reconciliation & BI Production Rollout

Audit against audited financial statements, data validation, and BI visualization connection.

Pricing Guidance

Estimated Investment

Target Investment

USD 5,800

Includes cloud architecture, ELT pipelines, dbt modeling, and reconciliation of up to 5 commercial data sources.

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Real-World Proof

Impact Case Study

From 3 Spreadsheets and Conflicting Numbers to Single Source of Truth

Initial problem

Subscription business with $3M ARR operating across 4 countries unable to determine channel profitability due to 40% discrepancies between Stripe, HubSpot, and Google Analytics.

Technical intervention

Deployment of BigQuery + dbt unifying Stripe, HubSpot, Google Ads, and Meta Ads with customer ID deduplication.

Outcome achieved

Discovered that their supposedly top marketing channel was losing money due to high early churn, allowing reallocation of $120K in ad spend to profitable cohorts.

Clarifications

Frequently asked questions

Why do we need data infrastructure if our CRM already has reporting?

CRM reports only capture actions recorded in that specific tool. They have no visibility into server hosting costs, payment gateway refunds, or ERP billing ledgers. Dedicated data infrastructure brings all systems together to calculate true net margin.

What ongoing cloud costs will this create for our business?

Very minimal. Modern cloud data warehouses (BigQuery or Snowflake) use serverless pay-per-query pricing that typically costs under USD $50 to $100 per month for mid-market businesses.

How do you ensure our commercial data remains private and secure?

The entire infrastructure is deployed inside your own cloud account (GCP or AWS). You maintain complete ownership of credentials, encryption keys, and role-based access policies with zero third-party intermediaries.

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