Services
Everything here is data and AI advisory, end to end: from instrumentation at the source, through the platform, to the reports and models the business runs on. You can engage me on one narrow problem or across the whole chain.
The 7 practice areas below are how I organize the work. Most engagements touch 2 or 3 of them, because platform problems rarely respect category lines. A cost problem is usually an architecture problem. An AI problem is almost always a data quality problem underneath.
The practice areas
Data strategy and architecture
The target state, the roadmap to it, and the platform design underneath: warehouse and lakehouse architecture, multi-cloud posture, and a data and AI operating model that fits your business. Where your data strategy gets written down and gets real.
Platform audit and cost management
A fixed-scope review of what your platform costs, what it delivers, and where the gap is. Ranked findings, then implementation. This is also the most common way an engagement with me starts.
Data engineering and operations
Pipelines, environments, CI/CD, migrations, observability, data quality infrastructure, and disaster recovery. The unglamorous layer that decides whether every other layer works.
Governance, privacy, and security
Ownership, catalogs, lineage, access control, and privacy compliance across GDPR, CCPA/CPRA, HIPAA, and SOC 2. Built as an operating capability, and enough to satisfy an auditor.
AI architecture and enablement
Agentic systems, LLMOps, evals, and AI readiness. The architecture that takes AI from a demo to a production system, starting from an assessment that matches the solution to the business need and to the organization that will run it.
Analytics and reporting
KPI standardization, executive dashboards, and instrumentation. The layer where the business actually meets the platform, and where trust in the numbers is won or lost.
Engineering leadership and delivery management
The delivery system I’ve deployed at 3 companies: underperforming teams turned around to quantified, forecastable work hitting 95%+ of commitments. Available as advisory, or embedded through a fractional CDO/SVP engagement.
How these connect
The areas cross-link on purpose. An audit (area 2) produces the findings a strategy (area 1) turns into a roadmap. Governance (area 4) is the precondition for AI (area 5): a model grounded in ungoverned data amplifies every problem in it. And delivery management (area 7) is what makes the other six land on a date.
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Not sure which area your problem lives in? That’s normal, and diagnosing it is my job, so bring the problem as you see it.