Database Design & Modeling
Selected work demonstrating this service.
- Designed, implemented, and administered 6 ETL/ELT pipelines, utilizing Google BigQuery, MSSQL, PostgreSQL, Shell scripting, PL/pgSQL, and Transact‑SQL, integrating data for efficient Python API processing.
- Architected, created, and managed 100 PostgreSQL, MS SQL, and Google BigQuery data warehouse databases with primarily GIS and time‑series data, optimizing performance and scalability.
- Architected a layered maritime platform separating a Next.js PWA frontend, a Go (Huma/Fiber) API, and a PostgreSQL function layer, keeping all business logic in the database.
- Designed a JSON passthrough architecture where PostgreSQL functions return complete JSON forwarded verbatim by the Go API, eliminating intermediate unmarshalling and decoupling the frontend from schema changes.
- Designed a PostgreSQL function‑first data layer across the platform's domain schemas (identity, organization, review, message, notification and more), exposing all data access through stored functions.
- Adopted UUID v7 time‑ordered identifiers (PostgreSQL 18) as entity keys to reduce B‑tree index fragmentation and speed up queries.
- Implemented a catalogue‑driven deep‑merge for stored JSON preferences, preventing missing‑key crashes as the schema evolves.
- Modeled the maritime domain — professionals, companies, ships, jobs and reviews — into normalized PostgreSQL schemas with SMALLINT lookups and UUID v7 keys.