I'm André Figueiredo — Data Engineer working across dbt, Microsoft Fabric and Databricks, and building production-grade AI agents on top of governed data. This site is a showcase of what I build and how I build it.
Belo Horizonte, Brazil · Data Engineering · DBT · Fabric · Databricks · Azure · AI Engineering · Agents · RAG
Dashboards nobody trusts? That's exactly what dbt fixes for me: tested, documented analytics layers — every metric with an owner, a test, and lineage.
Data scattered across silos? I unify it all in Microsoft Fabric — from OneLake to Power BI — into a platform the business team can actually use.
Pipelines breaking at 3am? I design lakehouses on Databricks with medallion architecture, Delta Lake, and orchestration that warns before it breaks.
Pipelines with no versioning or deploy pipeline? I orchestrate ingestion and ETL in Azure Data Factory and put it all under CI/CD in Azure DevOps — pipelines that are tested, reviewed, and shipped without manual deploys.
Most AI projects die at the demo. Mine start with what everyone else leaves for later: real architecture, retrieval over governed data, validation, and metrics.
A sales team drowning in five years of data needed answers, not another dashboard. I built an agent that talks to the database (text-to-SQL over PostgreSQL), retrieves qualitative context (RAG over Qdrant), and delivers action-ready sales-improvement reports — backed by a full engineering harness: scoped rules, validation hooks, MCP integration, subagents, and metrics.
Multi-step agents with scoped tools, memory, and guardrails, not single prompts.
Retrieval pipelines grounded in governed, versioned data — not just a vector store.
Metrics, validation hooks, and test harnesses that catch regressions before users do.
A sales team drowning in five years of data needed answers, not another dashboard. I built an agent that talks to the database (text-to-SQL over PostgreSQL), retrieves qualitative context (RAG over Qdrant), and delivers action-ready sales-improvement reports — backed by a full engineering harness: scoped rules, validation hooks, MCP integration, subagents, and metrics.
View repository ↗Modular modeling + tests + docs over a public dataset.
End-to-end Fabric lakehouse + Power BI semantic model.
I'm a Data Engineer based in Belo Horizonte, Brazil, working across dbt, Microsoft Fabric, and Databricks — building analytics layers and lakehouses that teams can actually trust and query with confidence.
What pulled me toward AI engineering was simple: agents and RAG systems are only as good as the data underneath them. So I build the platform and the agent together — governed data feeding retrieval and reasoning that actually holds up in production.
Same discipline either way — whether the artifact is a SQL model or an LLM agent: tested, documented, versioned, and built to be trusted, not demoed once and forgotten.
Always open to trading ideas on data platforms, AI engineering, and how to make the two work together — find me on GitHub or LinkedIn.