Data platform & AI engineering
Your pipelines are probably lying to you.
Most data failures never throw an error. They load zero rows, duplicate a join, or drift a vector space. They do it quietly, for months, feeding the dashboards your executives read and the models your business has already started trusting. I find those, fix them, and build the platforms that stop doing it.
A join fan-out inflated a curated dataset 2.5×. No job failed. No alert fired. The duplicated rows fed a production model for eight months before anyone questioned the counts. This is the shape of most data incidents: not an outage, but a number that quietly stopped being true.
Delivered on site for
Department of Veterans Affairs · Defense Intelligence Agency · Federal Bureau of Investigation · National Geospatial-Intelligence Agency · Defense Security Cooperation Agency · Pratt & Whitney · Constellation Energy · GEHA · University of Missouri Health Care
Credentials
Databricks Certified Professional Data Engineer
Microsoft Certified: Power BI Data Analyst
Active TS/SCI with CI Polygraph
Dual MS and MHA, Bioinformatics
Services
Four things teams hire me for
Usually several arrive together. The platform needs modernizing, something on it is already broken, and there is an AI initiative depending on both.
AI & ML data enablement
The model is the easy part. The hard part is the supply chain feeding it: training sets that reflect reality, embeddings that refresh when the source changes, and monitoring that tells you when it quietly stops working.
See how it breaks →Lakehouse modernization
Medallion architecture, Delta Lake, Unity Catalog migration, and incremental loads that actually stay incremental instead of quietly reverting to full reloads nobody costed.
See the runtime math →Pipeline rescue
Something has been wrong for months and nobody can say what. Silent zero-record loads. Race conditions. A filter hardcoded to a year that has already passed. I find it, prove it, and fix it.
Run the failure →Geospatial data engineering
Projections that silently disagree, geocoding that drifts, boundary files that stopped matching the addresses joined against them, and volumes that outgrew a single machine.
See the bad join →Tell me what's broken.
A short description of the platform and what isn't behaving is enough to start. I'll tell you within a day whether it's something I can help with.