Alwyn
D'Souza
Data & AI Engineering Leader
Building production-grade data systems that scale for enterprise teams.
- —DataOpsPipeline automation & operational excellence
- —dbtData transformation at enterprise scale
- —DatabricksLakehouse architecture & Unity Catalog
- —AI AgentsLLM-powered data automation via MCP
- —Modern Data PlatformsCloud-native analytics architectures
About
The person behind the stack
Core focus areas
I've spent 20+ years in the trenches of enterprise data — migrating off legacy Oracle stacks, rebuilding pipelines that kept breaking at scale, and eventually landing on the modern lakehouse patterns I work with today.
Most of my career has been in industries where data quality isn't optional — telco, banking, retail, infrastructure. The kind of environments where a bad cohort query costs real money, and a broken pipeline at 2am is your problem.
My current obsession is where data engineering meets AI — not the hype version, but the practical one. Using agents and MCP to handle the operational grunt work: catching schema drift before it breaks downstream, running lineage-aware tests automatically, giving business teams governed access to analytics without a ticket queue in the middle.
I write to share what actually works in production, not what looks good in a conference talk. And I build open-source reference architectures so teams don't have to start from scratch.
“Great data platforms don't just move data — they enforce trust, enable autonomy, and get out of the way of the business.”
20+
Years Engineering
Enterprise data platforms
50+
Enterprise Projects
Shipped to production
15+
Engineers Led
Cross-functional teams
3+
Cloud Platforms
AWS · GCP · Databricks
Expertise
What I Build
Deep specialization across the modern data & AI stack — from raw ingestion to production AI systems.
DataOps
End-to-end pipeline automation, CI/CD for data, observability, and operational excellence across the modern data stack.
dbt & Transformation
Production dbt architectures with data contracts, semantic layers, CI/CD pipelines, and advanced Jinja macros.
Databricks & Lakehouse
Unity Catalog governance, medallion architecture, streaming pipelines, and PII protection on Databricks.
AI Agents & MCP
LLM-powered automation, context-aware AI code review, MCP servers for governed data access, and agentic workflows.
Semantic Layers
Metrics-as-code with MetricFlow and dbt, DuckDB local-first stacks, and self-serve analytics for business teams.
Architecture Gallery
Platform Thinking
Technical architecture diagrams for production data and AI systems. Click any diagram to explore in detail.
Tech Stack
Tools & Technologies
The modern data & AI stack I work with daily — from lakehouse platforms to agentic automation.
Data Engineering
Lakehouse, pipelines, streaming
Cloud Platforms
Multi-cloud infrastructure
AI & ML
Agents, LLMs, ML pipelines
DevOps & DataOps
CI/CD, quality, automation
Analytics & BI
Dashboards, metrics, self-serve
Programming
Languages, frameworks, tooling
Insights
Writing from the Stack
Practical deep-dives on data engineering, AI agents, and modern platform architecture — written from the trenches of production systems.
Get in Touch
Interested in Data Platforms, AI Engineering, or Enterprise Architecture?
Let's connect. I'm open to consulting engagements, advisory roles, and technical collaboration.
GitHub
@alwyndsouza
Open-source reference architectures
alwyndsouza
Professional network & announcements