Strategic architect at the intersection of AI, automation, and enterprise systems.
I thrive at the adding calrity to technology, systems design, and innovation, transforming complex, abstract challenges into tangible, scalable solutions. I build AI roadmaps, automation frameworks, and intelligent processes across Salesforce, Dynamics, NetSuite, Jira, Airtable, and data ecosystems.
I focus on making the intangible understandable: documenting architectures, defining guardrails, and designing workflows that are observable, usable, and safe for teams with mixed technical fluency.
What I optimize for: scale, clarity, and measurable outcomes.
Core technologies across programming, integration, analytics, and automation.
Roles focused on enterprise AI, automation, and systems design.
Driving AI and automation strategy across Customer Success, Finance, and Operations within the Snowflake, Salesforce, and NetSuite ecosystem.
Modernized enterprise systems and analytics; built foundations for scalable automation through governance and reporting.
Directed operational technology modernization integrating data, automation, and knowledge systems across transmission infrastructure.
Built automation, reporting, and documentation standards to improve throughput and data integrity across operations.
Formal study supporting analytics, systems thinking, and applied problem-solving.
Data Analytics and Visualization (2019)
Examples of systems rebuilt for clarity, control, and real-world adoption.
AI-assisted Jira intake + coaching layer that turns vague requests into Definition-of-Ready tickets using a structured readiness score.
End-to-end procurement data pipeline that unifies Purchase Orders and Contracts into a governed, analytics-ready model for spend visibility and compliance.
Slack-based context retrieval and summarization across Salesforce and Jira, with ranked signals and confidence for faster issue triage.
Fail-closed subscription state automation with logging, auditability, and human review for amendments and edge cases.
Conversion and visibility degradation detection with eligibility thresholds, baselines, and routing that support action before revenue impact compounds.
Rebuilt a fragile legacy intake workflow into a maintainable Salesforce process that internal teams could understand, support, and extend.
Exploration: ranking search results by predicting a “match” signal using ML + face recognition concepts.
Exploration: comparing historical + current patterns to support trip planning and decision-making.
