About
A structural engineer for your data platform.
Jesse Smit
Founder, Smitster Data · LinkedIn ↗
I've spent over a decade building, scaling, and rescuing data platforms — from SQL Server and Azure BI estates to enterprise-scale Snowflake and Databricks lakehouses, and the AI systems now being layered on top of them. Smitster Data is the independent practice through which I bring that judgment to teams who need it without adding headcount.
My work sits at the seams: where ingestion meets transformation, where cost meets architecture, and increasingly where AI meets a data estate that was never designed to support it. I don't sell tools or resell cloud credits. I tell you what's actually going on, what it will cost you, and what to do about it — in language your engineers and your CFO can both act on.
Recent work includes modernizing a Fortune 500 enterprise data platform — well over 4,000 dbt models across Snowflake, Databricks, and Azure, provisioned entirely as code with Terraform — and cutting cloud spend by $10K+/month along the way. I've also built AI-agentic tooling, via the Model Context Protocol, that reviews production code changes and helps teams ship faster without lowering the bar.
I keep client work confidential. You won't find logos or named references on this site, because the teams I work with value discretion — and because the quality of the thinking should speak for itself. The Resources page has write-ups on real, anonymized outcomes from recent engagements.
The stack I work in
Warehouses & lakehouses
Transformation & processing
Cloud & orchestration
AI architecture
Tools change; the architectural judgment about how to fit them together is what carries over. That's what I'm hired for.
Track record
A decade-plus of platform work, in specifics.
Representative examples of the kind of work behind Smitster Data — real outcomes, real numbers, without naming the clients.
End-to-end architecture
I own systems from design through production, not just one layer.
I’ve built full BI platforms from the data warehouse up — source ingestion, ELT orchestration, semantic modeling, and reporting — deployed and maintained entirely through automated pipelines. On my current engagement that’s an environment spanning over 4,000 dbt models, provisioned entirely as code with Terraform and Azure DevOps.
Performance turnarounds
Specializing in fixing what wasn’t working.
Rebuilt an underperforming Power BI semantic model from scratch in a week — replacing six months of a previous consultant’s work — and cut report render times from 2–3 minutes to under 2 seconds. Elsewhere, cut a 13-hour daily data-sync window down to 5 hours, and took ETL jobs that used to run for days down to minutes.
Cost & infrastructure optimization
Fast isn’t enough — it has to be cheap to run too.
On my current platform engagement alone, infrastructure optimization — credit usage, cluster sizing, pipeline efficiency — is cutting recurring cloud spend by $10K+ a month. It’s a recurring pattern, not a one-off: I’ve delivered similar recurring Azure savings by fixing architecture bottlenecks on other enterprise platforms, and built cost-visibility tooling that let one client retire unused databases, reports, and pipelines outright.
AI & automation
Applying AI where it actually earns its keep.
Built and deployed an AI PR-review framework — powered by GPT-5-mini via MCP — that acts as one of two mandatory reviewers on every production code change, enabling autonomous, reasoned approvals without lowering the quality bar. Extended that into a multi-agent framework with a hierarchical skill library letting AI agents pick up and deliver backlog work on their own, and I’m now leading the ontology work to make the platform natively legible to AI agents, not just BI tools.
Get started
30 minutes can settle the decision you’re stuck on.
Send me the question and a little context. If it's a fit, I'll reply by email with scheduling details for a focused 30-minute session.
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