We build AI products. We do not sell hours.

Background

I started in mechanical engineering. An interview turned me toward data instead, and that detour became business school, then a few startups, then a decade building the infrastructure underneath other people’s decisions. The work I have spent the most time on is the unglamorous half of the discipline: pipelines that have to be right, migrations that cannot lose a row, and systems where “it usually works” is not an acceptable state.

That has mostly meant regulated data. I have led a hospital revenue-cycle modernization — refactoring more than a hundred fact and dimension tables and the reporting models over them, sourced from Epic Clarity and Caboodle — so healthcare data structures are familiar ground rather than a vertical I picked. Before that: data quality and reconciliation on syndicated credit facilities at a major US bank, where the daily job was proving that two systems agreed and finding out why when they did not; statewide transportation pipelines feeding a state’s public mobility reporting, delivered 30% faster than the ETL they replaced; analytics for an energy utility’s portfolio of digital products; IoT and SaaS telemetry at a consumer-electronics manufacturer; and ISO-9000 audit work early on, which is where I learned that a control nobody can evidence is not a control.

Most recently I migrated a player-cohort analytics platform onto a Databricks medallion architecture and built the config-driven framework that proved every migrated table matched its source — 678 million rows, 114 automated checks on every run. The migration was not really the deliverable. The evidence that it was correct was.

Today I work as a lead consultant in data engineering, building lakehouse platforms on Databricks — streaming ingestion through Delta Live Tables, governance in Unity Catalog, deployment through version-controlled Asset Bundles. The assignment underneath is the same in every industry: get the platform to the point where a published number can be traced back to its source, then make that traceability automatic instead of heroic.

Audit, reconciliation, and provable correctness are the recurring theme of my career, and that discipline is what the two products above are built on.

What I am building now

The current thread is AI-native data engineering — platforms designed for agents to work inside, and using agents to accelerate the engineering itself. In practice that has meant a ninety-odd-skill Databricks engineering catalog served live to AI coding agents over Model Context Protocol, with a usage-telemetry loop that decides what gets written next, and agentic systems that automate the translation and validation a migration otherwise repeats by hand. Jackdaws is that discipline, packaged as a product anyone can subscribe to rather than something built once for one client.

The Clinician Referral Engine is the same discipline pointed at a different buyer: a private practice that needs referral growth to be something it operates rather than something that happens to it, with the same insistence that a claim on the page trace to evidence rather than to confidence.

Built in public

The work is developed in the open, and the artifacts are the record. Decisions live in version control with their rejected alternatives written down. When a choice was hard to reverse, the reasoning is committed next to it. When something is unverified, it is labelled unverified rather than quietly rounded up — the same discipline that governs every claim on this site.

What I am looking for

People building or buying software with a low tolerance for hand-waving. If you have asked a vendor how something worked and not gotten a straight answer, we will probably get along.

The engineering work behind all of this — case studies, the full stack, the certifications and who issued them — is at jonathanhazeley.com. You can also find me on LinkedIn.

Outside the work I box, dance salsa, bachata and merengue, and build furniture in a shop I document about as carefully as I document a lakehouse. The through-line, if there is one, is a preference for practices with an honest feedback loop: the round, the dance floor, and a joint that either closes or does not.