Quantitative infrastructure for executive decision velocity
SQL / Python
core languages
dbt / Snowflake
warehouse modeling
Causal Inference
statistical rigor
Ingestion Validation
Semantic Modeling
Statistical Inference
Executive Reporting
Rigorous schema testing, anomaly detection, and automated validation before data enters the core warehouse environment.
Modular transformations in dbt ensuring metric consistency, clear lineage, and reproducible definitions across teams.
Advanced causal models, cohort retention tracking, and experiment evaluation designed to separate signal from noise.
High-density information design delivering clear, actionable insights that drive direct commercial outcomes.
Semantic consistency and reproducible analysis outrank superficial dashboard aesthetics every single time.
Core technical domains
Specialized proficiency across warehouse engineering, statistical modeling, and BI orchestration.
Warehouse & Ingestion
Statistical Modeling
Orchestration & BI
Architecting resilient data pipelines, optimizing Snowflake and BigQuery performance, and managing version-controlled transformations with dbt.
Executing survival analysis for churn cohorts, difference-in-differences experiment evaluation, and robust causal inference in Python.
Building high-density decision tools in Looker and Observable, maintaining Airflow schedules, and delivering clean executive reporting.
Explore reproducible repositories or discuss analytical advisory.