Data Engineering

Full Description

Every AI investment fails without a solid data foundation. Before you can build predictive models, train ML systems, or trust your dashboards, your data needs to be unified, clean, and flowing in real time. That's what our Data Engineering engagement builds.

We design and implement the full data stack: ETL pipelines that pull from every source system, a centralized warehouse as the single source of truth, transformation models that produce clean, business-ready data, and dashboards that give every team member the numbers they need without waiting for someone to pull a report.

Key Deliverables

Our Process

Weeks 1–2: Data audit and architecture design. Weeks 3–6: Pipeline build and warehouse setup. Weeks 7–9: Transformation models and dashboard build. Weeks 10–12: Testing, monitoring setup, and handoff.

Is This Right for You?

Ideal Client Profile

Data lives in 5+ disconnected systems with no single source of truth | Team spends hours weekly reconciling numbers across tools | AI or analytics investments are blocked by unreliable data | Revenue is $3M+ and data infrastructure hasn't kept pace with growth | Ready to invest in the foundation before the AI layer

You’re on at least 3 SaaS tools that don’t talk to each other natively

Revenue is $250K–$50M ARR and operations are becoming a growth bottleneck

You want the system built correctly once — not a Zapier patchwork that breaks monthly