Data engineering · Fintech, capital markets & Web3

I build and rescue data platforms that survive an audit.

Databricks and Spark for fintech and Web3 teams — medallion architecture, dbt governance, and pipeline observability. The kind of platform where you can answer “where did this number come from?” without a two-day investigation.

DataBlock is one person — me. No agency, no account manager, no handoff to someone you haven't met. You get a small number of engagements' worth of attention, and the person you talk to is the person who builds it.

Available for contract work · open to forward-deployed engineering roles · more on that

Three ways to work together

Platform audit

Two weeks, fixed fee

A written assessment of what your pipelines actually cost, where they break, and what an auditor would find. Ends in a document you can act on without me.

Build engagement

Monthly retainer

Medallion architecture, dbt governance and pipeline observability, built to survive handover — your team owns it when I leave.

Fractional platform lead

Ongoing

Standards, review and direction for a data team that has the engineers but not yet the platform practice.

What I'm building

Status pulled live from each project's repository

Full status

DataBolt

A public benchmark for data engineers.

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A reproducible pipeline benchmark on an open dataset, scored on dollars per terabyte and terabytes per hour. Correctness is a gate, not a score — the competitive surface is which architecture you chose.

Data-Citizen

An agentic overlay on NYC Open Data.

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A normalised catalog of NYC open data with five-dimension quality scores and a dataset relationship graph, persisted so another application can query the layer instead of re-solving ingestion against raw Socrata endpoints.

Build City

NYC · community

A community of people in New York actually shipping things. I'm its CTO — the technical half, helping members get their projects out the door. It is open, it is not a DataBlock product, and you do not need to hire me to turn up.

Stay updated

Occasional notes on data engineering, pipeline cost, and what the lab turns up.