Data & analytics engineer focused on the parts that actually decide whether enterprise analytics works in production — semantic layers, data governance enforcement, BigQuery performance and cost, Looker at scale, and the tooling around them. Also building production agentic systems: Looker MCP servers, semantic-layer-as-API patterns, and agentic analytics on top of BigQuery.
- From Chaos to Trusted Agents: Master the Semantic Standard with LookML — Google Cloud Next '26, Las Vegas · Speaker profile
- Mentor, Dell x NVIDIA AI Hackathon — Local AI on Dell Pro Max with GB10 — Seattle (remote) · Event page
- Judge, Global South AI Hackathon 2026 — Apart Research · Details
- Reviewer, IJCAI-ECAI 2026 Workshop — GlobalSouthAI · OpenReview
- Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents
- From Policy to Enforcement: Operationalizing Data Governance in Enterprise-Scale Analytics Platforms
- Semantic Layers May Become the API Layer for AI
- Deploying Looker MCP at Enterprise Scale: What Actually Works and What Doesn't
- Classification Drift in AI-Augmented Data Analytics: A New Attack Surface for PII and PCI Exposure
- From Semantic Layers to AI-Native Data Interfaces: Evolution, Challenges, and Governance
- IT-IFC: Ingestion-Centric Information Flow Control for Preventing PII/PCI Leakage in AI-Augmented BI
- small library so my LLM agent stops double-charging
| stream-ingest-kafka | Real-time ingestion pipeline: Redpanda (Kafka-API compatible), Avro + Schema Registry, exactly-once producer→consumer→sink verified against a real crash, dead-letter queue for semantically invalid events, backward-compatible schema evolution |