I build scalable data platforms and AI-powered products that drive business outcomes. Currently leading strategic initiatives across data mesh architecture, semantic layers, and modern data stack implementation.
- Data Engineering & Lakehouse: Data Mesh · AWS Glue · Lake Formation · Iceberg · Databricks · dbt · Redshift · GCP
- DataOps: Data Contracts · CI/CD for Pipelines · Semantic Layers · End-to-End Observability · Incident & Change Management
- AI & ML Engineering: · AIOps · Production LLM Integration · Agentic Pipelines (MCP) · RAG · AI Guardrails for Data Quality
- MLOps: Pipeline Orchestration · Model Lifecycle Governance · ML Observability
- Streaming: Flink · Kafka · Redpanda · RisingWave
- Infra & Orchestration: Terraform · Step Functions · GitHub Actions · dlt
- Leadership: COE Capability Uplift · Vendor Engagement (dbt Labs · AWS · Databricks) · Technical Mentorship
- A Better Way to Do Real-Time Streaming (Redpanda + RisingWave Explained)
- BI-as-Code: Bridging the Gap Between Data Engineering and Analytics
- MCP vs Agentic Skills — What’s the Difference, and How Do They Work Together?
- Databricks transitioning to the Direct Deployment Engine for Declarative Automation Bundles
- The dbt MCP Server: How the Model Context Protocol Bridges Governed Data to AI Agents
- Metrics as Code: Building a Semantic Layer With dbt and MetricFlow
- Why Data Teams Need a Semantic Layer
- dbt fusion — Under the Hood — The Technical Architecture
- The Evolution — Why dbt Needed a Revolution
- Speed Up Your dbt Development with Sample Mode