Data Engineering

• Designed a comprehensive infrastructure framework for DTU impacting 14 departments, featuring flexible modules, unified data pipelines, and structured support strategies for long‑term adoption.
• 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.
• Delivered 8 Power BI projects with comprehensive manuals, integrating Microsoft Power BI tools with NodeJS API and Python FastAPI for effective data analytics and visualization.
• 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.
• 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.
• Optimized forecasting and investment strategies for 11 electricity grid operators, driving operational efficiency through data‑driven GIS solutions.
• Led the development, deployment, and support of over 30 GIS projects, demonstrating expertise in PostgreSQL, Bash, Python, JavaScript, GDAL, ArcGIS, PostGIS, and Mapbox technologies.
• Developed a Data Analytics reporting system, increasing quarterly software revenue by 400% through Python‑based PDF reports.
• 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.
• 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.

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

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