Most AI projects
don't fail because
of the AI.
They fail because
of bad data.
If your team spends more time debating which number is right than acting on it, that’s a data infrastructure problem … not a people problem. Our data engineering services fix that foundation so everything above it can work and your team can be far more productive.
Average Annual Cost of Poor Data Per Organization
Bad data isn’t a minor inconvenience, it compounds every quarter your infrastructure stays broken.
– IBM Institute for Business Value.
After We Engage
"
Our analysts spend 80% of their time cleaning data. We built an AI model and it gave us garbage outputs. Our dashboards show different numbers depending on which system you look at.
What we hear from almost every new data engineering client.
OUR DATA ENGINEERING SERVICES
End-to-End
Data Platforms
AI-Ready.
We deliver data foundations that make AI practical clean, governed, and production-ready from ingestion to feature stores.
73%
Goes Unused
85%
at the Data Layer
10x
Infrastructure Is Built Right
Build pipelines that capture and deliver data on time connecting every system in your stack.
Automate quality, trace lineage, and visualize what matters so decisions are made on data you can defend.
Deploy cloud-scale storage and operationalize the features your ML models need to train and serve.
Ready to build the data foundation your AI strategy needs?
Tell us about your data challenges. We’ll map the path forward.
Customer Success Stories
Loop News Web
Delivering Smart, Personalized Breaking News to Millions Across the Caribbean
-
30%
faster content management -
99%
uptime reliability -
3М+
active users reached -
67%
more user engagement
Doc Talk®
Secure, Efficient Clinical Handoff Among Healthcare Teams
-
85%
faster activity logging -
75%
fewer manual errors -
90%
physician adoption -
70%
quicker resolution
THE TRANSFORMATION
What Changes When the
Data Foundation Works.
Sales says $4.2M.
Finance says $3.8M.
One source of truth.
One number. Every team.
Analysts cleaning CSVs
80% of the week.
Automated pipelines.
Analysts doing analysis.
AI worked in the lab.
Broke in production.
Production-grade
AI data infrastructure.
DCD designed and delivered a robust API architecture that helped triple our platform adoption within six months while continuing to support us as we scale.
Kevin Yang
Senior IT Director, Genentech
DCD brought elite development talent and a smooth, professional process that led to a successful, results-driven eCommerce website launch.
Paul Popoff
Director of Marketing, Moon Valley Nurseries
Dot Com Development turned our vision into a clear roadmap and guided us seamlessly from planning to launch. True strategic partners.
Jeff M.
The PathMaker, LLC
Data Engineering Services Common Questions
How do I know if data engineering services are what my business actually needs?
If your team argues over which report is right, analysts spend more time cleaning data than analyzing it, or AI initiatives keep stalling before production ... those are data infrastructure problems, not people problems. We offer a free data readiness assessment to confirm the diagnosis before any commitment.
What does a data engineering engagement with DCD actually look like?
Every engagement starts with a scoping and audit phase where we map your current data landscape. From there we produce a fixed-cost proposal with clear scope, timeline, and milestones. You always know what's being built, when it delivers, and what it costs — before work begins.
Will your data engineering services work with our existing cloud setup?
Yes. Our data pipeline development and governance patterns are cloud-agnostic. We work across AWS, Azure, and Google Cloud and can design for hybrid or multi-cloud environments. We start from where you are, not from a preferred vendor stack.
We already have a data team. Why would we bring in DCD?
Yes our pipelines and governance patterns are cloud-agnostic.
How long before we see results from data pipeline development?
Most clients see meaningful improvement in data reliability within the first 60–90 days. Full data warehouse and governance builds typically run 3–6 months depending on complexity. We sequence delivery so you're getting working value at each phase — not waiting for a big bang launch
Do we own everything that gets built?
All code, architecture, documentation, and IP transfers to you at project completion. Everything is stored in your private repositories from day one. We also provide full handoff documentation and optional support retainers so your team is never dependent on us to keep things running.
Not sure where to start?
- A Data Readiness Audit
- Scope, cost & timeline clarity
- A 30-60-90 day action plan
Let’s talk.
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