Feature engineering systems
Processes transform raw data into structured inputs that are better suited for learning and inference.
What It Is
Data Warehousing is the storage and management of large datasets optimized for reporting and analysis. On this site, it matters because it transfers across technical, operational, and venture work instead of staying trapped in one narrow context.
Learn more: https://en.wikipedia.org/wiki/Data_warehouse
Geospatial Human Risk Quantification
Using SafeGraph location observation data and Databricks/Spark, built a geospatial model to quantify human risk around utility infrastructure — helping...
Geospatial Systems
Systems model physical space using coordinates, polygons, and clustering to derive insights and build products from location data.
GIS / geospatial systems
Spatial systems represent and analyze location-aware data so geographic relationships can be integrated into products and decisions.
Google BigQuery
Google BigQuery is a serverless data warehouse for large-scale analytics.
Machine learning systems
Pipelines train models on data to produce predictive or generative outputs and improve performance through iteration.
Model serving systems
Infrastructure deploys trained models for real-time or batch inference under latency and scaling constraints.
Proprietary Vendor Data Warehousing and Big Data Management
Managed proprietary and sensitive big datasets in Google Cloud using GCS, BigQuery, and Composer (Apache Airflow). Built Confluence documentation from scratch...
Query Optimization Systems
Systems evaluate and optimize queries before execution to reduce cost and improve performance.
Restaurant Franchise ETL and Data Model Development
Built accounting and point-of-sale data warehouse ETL processes and data models for restaurant franchise analytics including Blaze Pizza and other chains,...