Performance Tuning
17 Achievements
- Engineered hourly electricity consumption aggregation pipeline in Python / SQL / Bash + Jq, achieving 180ms for 30‑day datasets across heterogeneous JSONL sources.
- Accelerated geographical data pipeline performance by 50x by improving SQL programming and data modeling across PostgreSQL, MS SQL, and Google Cloud BigQuery.
- Improved geographical map application performance by 10x through strategic database transition from MSSQL to PostgreSQL, optimizing processing and data security.
- Resolved 1 000 issues in geographical data and time‑series data, using GDAL, ArcGIS, PostGIS, Mapbox, QGIS, SQL (PL/pgSQL, Transact‑SQL), Bash, ensuring high‑quality big data 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.
- Designed, deployed, and maintained 10 PostgreSQL and MS SQL servers on Ubuntu Linux VPS, ensuring optimal server performance and reliability.
- Accelerated PostgreSQL performance by 10x via strategic indexing, partitioning, and query optimization, enhancing database efficiency for user, tenant, geospatial, and time‑series electrical data.
- Directed full‑stack GIS map development, overseeing PostgreSQL, Mapbox, ReactJS, and NodeJS to deliver an integrated solution.
- Overhauled internal processes, saving 8,000 hours by improving software architecture, systems, and scheduling efficiency.
- Enhanced project efficiency, saving 150 hours per month across 30 projects by optimizing workflows and resource management.
- Administered 40 websites on Ubuntu Linux hosting servers with Apache and Nginx, ensuring high availability and performance.
- Engineered 600 PL/pgSQL‑based ETL/ELT pipelines to streamline complex data processing workflows across multiple PostgreSQL development and production environments.
- 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.
- Adopted UUID v7 time‑ordered identifiers (PostgreSQL 18) as entity keys to reduce B‑tree index fragmentation and speed up queries.
- Modeled the maritime domain — professionals, companies, ships, jobs and reviews — into normalized PostgreSQL schemas with SMALLINT lookups and UUID v7 keys.
- Built the Next.js 16 frontend with SSR, SSG and CSR strategies and a server‑shell prefetch‑and‑hydrate pattern that eliminated N+1 fetches.
- Delivered full Progressive Web App support — installable and offline‑capable — with Workbox runtime caching via next‑pwa.