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
Full data architecture design | ETL pipelines from all source systems | BigQuery or Snowflake warehouse setup | dbt transformation layer | Real-time dashboards and alerting | Runbook for ongoing operations
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.
✓Prioritized 90-day implementation roadmap
✓Tool selection & architecture design
✓Full build, testing & deployment
✓Team training & documentation
✓90-day post-launch support
Our Process
1. Data Source Audit
We catalog every system generating data in your org, identify gaps and duplication, and map the ideal data flow. You get a clear picture of what's broken and what's missing.
Weeks 1–2
2. Architecture & Modeling
We design your warehouse schema, transformation logic, and pipeline architecture. You sign off on the data model before we start building.
Weeks 2–4
3. Pipeline Build & Testing
We build your ETL/ELT pipelines, configure your warehouse, and validate data accuracy against source systems. Every pipeline is tested with real data before going live.
Weeks 4–8
4. Dashboard Delivery & Handoff
We build your BI dashboards, train your team on self-serve reporting, and hand off complete documentation. Ongoing monitoring alerts are configured before we leave.
Weeks 8–10
Discovery & Workflow Audit
We spend 1–2 weeks mapping your current processes, identifying automation opportunities, and calculating ROI potential for each. You get a prioritized list of workflows by business impact, not technical complexity.
Architecture Design & Sign-off
We design the full workflow architecture — tool selection, data model, error handling logic, and security approach. You review and approve before we write a single node.
Build, Test & Staging
Full workflow build in a staging environment. We run every edge case, test failure modes, and validate outputs against your real data before touching production.
Production Deployment & Monitoring
Coordinated production cutover, monitoring setup, and live observation for the first 72 hours. Full handoff documentation delivered within 5 days.
FAQ
How long does an automation engagement typically take?
Most automation engagements are complete in 6–12 weeks from signed contract to production deployment. The timeline depends on complexity — a single workflow can ship in 3 weeks, while multi-system enterprise builds take 10–14 weeks. We’ll give you a specific timeline after the discovery audit.
Do we need technical staff to maintain the automations after delivery?
No. We build with maintainability in mind. Simple parameter changes (like updating an email template or changing a threshold value) can be done by any non-technical team member. For structural changes, our ongoing retainer clients get direct access to our team. Otherwise, you can return to us for a scoped change request.
What’s the typical investment for an automation engagement?
Scoped automation projects start at $8,500 for a single workflow build. Multi-workflow packages range from $18,000–$45,000 depending on complexity and integrations. Enterprise implementations are priced on discovery. All engagements include a full ROI analysis before we ask you to commit — if we can’t justify the investment on paper, we’ll tell you.