Data Pipeline & Automated Reporting

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The Problem

Your Data Exists. Your Answers Don't.

The Current Reality

Revenue lives in Stripe, leads in HubSpot, ops in a spreadsheet
Someone manually exports CSVs and stitches them together in Excel
Monday morning reporting takes 3 hours of copy-paste
Decisions made on last week's data — or last month's
Numbers don't match between systems; no one knows which is right
No visibility into what's actually driving the business

After This Engagement

One source of truth that pulls from every system automatically
Reports arrive in your inbox — no one has to build them
Monday morning insights are ready before you open your laptop
All metrics updated on a schedule you define: hourly, daily, weekly
Definitions are locked in — revenue means the same thing everywhere
You know exactly what's working, what's not, and why

10+

data sources unified into one model

manual reporting hours after go-live

Real-time

or near-real-time refresh schedules

Common Use Cases

Who This Is Built For

💰

Revenue Intelligence

Unify Stripe, QuickBooks, and your CRM into a clean revenue model. Know MRR, churn, and LTV without touching a spreadsheet.

📣

Marketing Attribution

Connect ad spend (Meta, Google), CRM leads, and closed deals into a single funnel. Know what's actually converting.

🏥

Healthcare Operations

Aggregate patient data, scheduling systems, and billing records into a unified ops model for compliance and performance tracking.

📦

Inventory & Ops

Connect supply chain, fulfillment, and sales data to get ahead of stockouts, delays, and demand shifts before they hit.

👥

Team Performance

Track productivity, project completion, and resource utilization across tools like Jira, ClickUp, or your custom system.

🏦

Financial Reporting

Automate your P&L, cash flow, and budget-vs-actuals reports. Get CFO-level visibility without a CFO-level budget.

What You Get

Deliverables

✓
Data Source Inventory & Architecture Design — Catalog of every data source, a proposed unified schema, and a clear pipeline architecture before a single line of code is written.
✓
ETL / ELT Pipeline — Production-grade data pipeline (Python + dbt) that extracts from your sources, transforms into a clean model, and loads into your warehouse on your schedule.
✓
Data Warehouse Setup — BigQuery, Snowflake, Redshift, or DuckDB — set up, optimized, and secured in your cloud account.
✓
Automated Reports — Scheduled reports delivered to email, Slack, or your BI tool of choice (Looker Studio, Metabase, or similar).
✓
Data Quality Monitoring — Automated alerts when data looks wrong — missing records, broken pipelines, statistical anomalies.
✓
Metric Definitions Document — Every KPI defined, agreed upon, and locked in so there's no more arguing about what "active users" or "revenue" means.
✓
Cloud Deployment & Scheduling — Pipelines deployed to your cloud environment with orchestration (Airflow, Prefect, or Cloud Scheduler) managing execution.

How It Works

The Process

Data Discovery (Week 1)

We catalog your data sources, document access credentials and APIs, define the key metrics you care about, and sketch the unified data model.

Pipeline Architecture (Week 1–2)

Design the extraction layer, transformation logic in dbt, and output schema. You approve before build begins.

Build & Test (Weeks 2–4)

Pipeline built and tested source-by-source. Data quality checks run on historical data. You see clean output coming through before go-live.

Reporting Layer (Week 4)

Automated reports wired up — email, Slack, or dashboard — with the metrics and schedule you defined in week one.

Deploy & Handoff (Week 4–5)

Production deployment, monitoring setup, full documentation, and a 30-day support window so you're never left guessing.

Technology

The Stack

🐍 Python 🗃️ dbt (data build tool) 🔢 SQL ☁️ BigQuery / Snowflake / Redshift ⚙️ Airflow / Prefect 🌐 AWS / GCP / Azure 📊 Looker Studio / Metabase 🔗 REST APIs / Webhooks 💼 Stripe / HubSpot / Salesforce 📋 Google Sheets / Airtable