Capability map
Capabilities are grouped by how I work with them—not by inflated labels or percentage bars.
Strong / Practical Focus
Working Knowledge / Project-Based
AI-Assisted Technical Working Knowledge
Connected disciplines
The differentiator is not isolated tool knowledge. It is the ability to connect layers into a coherent solution.
Generative AI, LLMs, AI Agents, agent workflows, MCP, human oversight, AI automation and applied business use cases.
Ingestion, profiling, cleaning, validation, transformation, ETL/ELT, analytics-ready data, KPIs and reporting foundations.
Dashboard requirements, operational analytics, executive reporting, data storytelling, Power BI and Looker Studio concepts.
Google Cloud and BigQuery; Azure administration, security, data and DevOps concepts; AWS foundations and ongoing exploration.
Repeatable workflows, document and reporting automation, MCP tools, AI-to-data connectivity and orchestration concepts.
Technical writing, documentation, case studies, labs, course development, practice questions and professional training.
Technical practice
With Python and SQL, I can understand purpose and logic, modify AI-generated implementations, troubleshoot with assistance, validate outputs and use them within data/AI projects—without claiming advanced from-scratch programming from memory.