Data Pipelines (ETL/ELT)

• Designed a comprehensive infrastructure framework for DTU impacting 14 departments, featuring flexible modules, unified data pipelines, and structured support strategies for long‑term adoption.
• 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.
• 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.
• Automated GIS SaaS application deployment, data processing, and reporting system using GitHub Actions CI/CD, Python, Bash, and SQL.
• Automated delivery of 20 GIS data pipelines and app data ETL processes, streamlining infrastructure automation and reporting.
• Streamlined data analysis and software development processes, saving 4,000 hours by introducing GitHub, GitLab, Bash, and Python CI/CD practices.
• Automated data processing tasks using Shell scripting, PL/pgSQL, Python, and Transact‑SQL, increasing productivity and efficiency.
• Engineered 600 PL/pgSQL‑based ETL/ELT pipelines to streamline complex data processing workflows across multiple PostgreSQL development and production environments.
• Architected, developed, implemented, supported infrastructure, data processing, and the map application for 2 years non‑stop without any weekends, holidays, or vacations, 10–14 hours a day.
• Built the Python vessel‑data scrapers (MarineTraffic, Maritime‑Database) and a repeatable import that seeds the platform's reference data — 184,197 rows, including 698 companies and 56,149 vessels.
• Generated 495 achievement pages across three languages from a read‑only SQLite export, with the page address authored as data so that correcting a sentence no longer moved the page and broke the link.

This work is part of what we offer as Data Pipeline Development (ETL/ELT), GIS & Geospatial Solutions, DevOps & CI/CD Automation, Data Governance & Quality, Database Performance Tuning, Backend & API Development, Data Analytics & BI Dashboards and Data Warehouse Design.

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