Solutions Architect @ Databricks · Building governed, production-grade Lakehouse and GenAI systems.
I work across the full breadth of the Databricks Data Intelligence Platform — from vibe coding with Genie Code and ZeroOps, to full-stack Databricks Apps, to agentic AI and serverless data architecture. My mission is to enable companies to move their projects forward following best practices, with a focus on optimization, governance, control, and observability — turning proofs of concept into systems teams actually run.
I work AI-first in my day-to-day: I design custom agents, skills, and plugins (with LLMs like Anthropic's Claude) to accelerate delivery — from internal automation to standing up customer POCs faster. Building reliable, governed AI tooling isn't just what I ship for others; it's how I work.
- 🏛️ Data governance & access control — Unity Catalog, lineage, access traceability, LGPD-aligned patterns
- 🤖 GenAI & agents on Databricks — RAG, Vector Search, Mosaic AI Agents, MLflow evaluation, Model Serving
- ⚡ Lakehouse architecture — Delta, serverless compute, cost optimization, Lakeflow pipelines
- 📊 AI/BI & self-service analytics — Genie, AI/BI dashboards, metric views
- 🧱 Platform as code — Terraform, Databricks Asset Bundles, CI/CD
| Project | What it is |
|---|---|
| dbx-gov-access-lens | Databricks App for centralized access governance — shows what a user/group can access and where each grant comes from (direct, group, or nested group), reading only system tables and native APIs. |
| databricks-agent-lakehouse-demo | End-to-end GenAI agent on the Lakehouse: ingestion → Vector Search → RLS governance → Mosaic AI Agent → MLflow evaluation → Model Serving → Databricks App, with Terraform infra. Synthetic data. |
See my full, verifiable set of Databricks certifications and accreditations on my credential wallet.
Architecture patterns, governance accelerators, and GenAI demos — reach out if any of it is useful to your team.