Performance Tuning

• A month of meter readings in 180 milliseconds

  Engineered hourly electricity consumption aggregation pipeline in Python / SQL / Bash + Jq, achieving 180ms for 30‑day datasets across heterogeneous JSONL sources.

• Made a geographical data pipeline 50x faster

  Accelerated geographical data pipeline performance by 50x by improving SQL programming and data modeling across PostgreSQL, MS SQL, and Google Cloud BigQuery.

• Improved GIS map performance 10x on PostgreSQL

  Improved geographical map application performance by 10x through strategic database transition from MSSQL to PostgreSQL, including the data cutover, optimizing processing and data security.

• Resolved 1,000 issues in geographical data

  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.

• Ran 100 PostgreSQL, MS SQL, BigQuery warehouses

  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.

• Ran 10 PostgreSQL and MS SQL servers on Ubuntu

  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

  Accelerated PostgreSQL performance by 10x via strategic indexing, partitioning, and query optimization, enhancing database efficiency for user, tenant, geospatial, and time‑series electrical data.

• Engineered 600 PL/pgSQL ETL/ELT pipelines

  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 from Postgres to API

  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.

• Quicker answers from a better choice of key

  Adopted UUID v7 time‑ordered identifiers (PostgreSQL 18) as entity keys to reduce B‑tree index fragmentation and speed up queries.

• Modeled 348 tables across 34 Postgres schemas

  Modeled the maritime domain into 348 normalized tables across 34 PostgreSQL schemas — professionals, companies, ships, jobs, reviews and the rest — with SMALLINT lookups and UUID v7 keys.

• Moved slow work onto a River job queue

  Moved slow work off the request path onto a River job queue — 15 worker modules, 8 scheduled tasks and 20 pg_cron jobs — so a request returns while the work behind it carries on.

• Built first‑party analytics inside PostgreSQL

  Built first‑party product analytics in PostgreSQL — 47 functions over partitioned event tables that prune themselves — pseudonymised behind a rotating salt and gated on the visitor's consent.

• Built a loyalty wallet, leagues and badges

  Built the loyalty and reputation system — 67 functions over a 31‑table ledger, with leagues, badges and a redemption shop — taking a row lock on the balance to close the double‑spend window.

This work is part of what we offer as Database Design & Modeling, Database Performance Tuning, Data Pipeline Development (ETL/ELT), Data Governance & Quality, Database Administration (DBA), GIS & Geospatial Solutions, Backend & API Development and AI Data Readiness.

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