Full-Funnel AI Marketing Analytics. A modern data stack powered by dbt MetricFlow and MCP. Natural language insights across Google/Meta Ads, CRM, and 5 data warehouses. Includes XGBoost lead scoring and a $0/mo architecture.

A Model Context Protocol (MCP) server that provides MetricFlow CLI tools

🚀 Modern web analytics platform built with Next.js 16 & TypeScript. Privacy-focused alternative to Google Analytics with real-time tracking, beautiful dashboards, and comprehensive insights. Features secure authentication, responsive design, and professional UI.

AI-augmented dbt Mesh on Snowflake — companion repo for the Medium

A production-grade Modern Data Stack reference implementation using dlt, dbt-core, and Databricks Unity Catalog, featuring a governed semantic layer with MetricFlow.

Context cards for dbt metrics — 5-layer meta: schema, validator CLI, and installable agent skill so AI agents answer analytical questions accurately

Tabular Intelligence — multi-tenant Text-to-SQL over user-uploaded structured files (CSV/XLSX/JSON/Parquet/Avro/...). Materialises uploads to Parquet on object storage, queried by DuckDB through a multi-agent pipeline (Grounding → Generation → Critic → Explainer). Part of Firefly OperationOS.

Testing dbt Fusion, dbt VSCode Extension, and MetricFlow by modeling Consumer Financial Protection Bureau (CFPB) database into a comprehensive star schema with semantic layer capabilities

E-commerce metrics layer built with dbt and BigQuery on the Olist Brazil dataset. Covers staging, intermediate, and mart layers across 6 business domains with a MetricFlow semantic layer. Includes RFM segmentation, ML feature stores, data contracts, source freshness tests, GitHub Actions CI, and self-hosted Metabase dashboards.

5 brand teams, 5 different net revenue numbers — same raw data. Governed dbt metrics layer eliminates divergence across 5 pharma commercial scenarios.

A realistic, executable dbt Core training fixture for learning analytics engineering beyond the first tutorial.

Multi-adapter dbt Data Vault 2.0 platform — Snowflake, BigQuery, DuckDB (CI), Postgres. Hubs/Links/Satellites + PIT/Bridge + Marts + Semantic Layer. Triple-orchestrated via Airflow + Step Functions + Control-M simulator. Zero-cost CI.

An AI analyst that answers only from a governed semantic layer and scores how much to trust each answer. dbt + BigQuery + Claude.

Testing MetricFlow with DuckDB and dbt — a lightweight sandbox to build, validate, and query semantic metrics locally.

W3C-standard ontology (SKOS/OWL/JSON-LD) + MCP server + Claude Skills. Captures business metadata once and delivers it to dbt, Semantic Layer, and AI/LLM contexts - bridging the 5 breakpoints where vocabulary gets lost downstream.

AI-powered semantic layer for BI: auto-generates metrics definitions, dimension hierarchies, certified datasets and natural language descriptions from raw BigQuery schemas using LLMs

Production-pattern dbt MetricFlow semantic layer with Snowflake Horizon Catalog integration and a FastAPI agentic query interface — governed metrics for AI systems, no raw SQL.