Data engineering
SQL models, ETL jobs, warehouse structure, BigQuery, APIs, and pipelines that keep reporting current.
Technology
This is the technical side of the practice: data engineering, APIs, cloud infrastructure, web builds, internal tools, and the practical engineering needed to make a business answer repeatable.
Technology as the build layer
The technology work is not separate from the strategy. It is how the strategy survives contact with daily operations: clean inputs, reliable jobs, useful interfaces, and a system someone can maintain.
I am not selling a narrow engineering title. I am selling the ability to understand the business problem, design the technical path, and build enough of it to make the answer real.
Main workstreams
Useful for corporate consulting, freelance work, and the business-card version of the site.
SQL models, ETL jobs, warehouse structure, BigQuery, APIs, and pipelines that keep reporting current.
Google Cloud systems, scheduled jobs, server logic, monitoring habits, and practical architecture for small teams.
Back-office apps, workflow automation, dashboards, and interfaces that replace brittle spreadsheet routines.
Focused websites, repairs, integrations, and launches where AI makes the economics of small builds make sense.
Where it shows up
AI implementation needs connected systems. Strategy needs models and data. Attribution needs collection, joining, and reporting. The technology page gives that throughline a home instead of hiding it behind the word services.
Relevant work
CRM and point-of-sale APIs from hundreds of locations into a warehouse, supporting customer lifetime value and retention models.
A cross-platform analytics implementation across 60 brand sites, with engineering coordination across web, iOS, Android, and mobile web.
A question is a good place to start
The numbers, the process, or the system behind them. Tell me where you’re stuck.