Data Engineer

Built a secure and flexible data lakehouse platform integrating multiple data sources, with architecture optimized for storage and query performance.

Engineered a Kubernetes-based Data Vault platform using Trino, MinIO, dbt, Airflow, and Python. Implemented hubs, links, and satellites to support scalable pipelines and complete lineage tracking.

Migrated a bank data warehouse from IBM DataStage to Oracle Data Integrator (ODI). Analyzed and converted legacy jobs into standardized ODI workflows while maintaining data integrity, reporting accuracy, and operational reliability.

Developed and maintained automated data crawling systems using Python, Selenium, and API integrations to extract and process data from batdongsan.com.vn. Designed daily batch ETL pipelines to produce clean, structured analytical datasets.

Created a PostgreSQL data mart for marketing and business teams to analyze impressions, views, clicks, and listing performance. Built and deployed interactive dashboards and reports with Apache Superset and Looker Studio.

Automated reporting workflows, reducing manual effort by 50%. Analyzed boosting campaign performance, contributing to a 30% reduction in campaign costs and a 25% improvement in customer interaction rates.

Built automated timesheet and cost analysis solutions for more than 100 staff members using Power BI, Python, Excel, and MinIO.

Integrated MinIO storage for drag-and-drop Excel uploads and developed Python ETL scripts to automate data processing. Created dynamic Power BI dashboards with DAX for real-time analysis of timesheets, project costs, revenue, and profitability.

Improved payroll efficiency by 50% and strengthened operational decision-making through automated reporting and analytics.

Designed and implemented an end-to-end financial data pipeline processing 2,702 daily SGX bid/ask quote datasets spanning 2015 to the present using Python and Apache Spark.

Automated downloading and extraction of 2,702 ZIP files into CSV files stored on Google Cloud Storage. Processed billions of rows with Spark and managed approximately 354 GB of source CSV data.

Optimized local analytical storage with ClickHouse, reducing disk usage to approximately 70 GB through compression. Analyzed price volatility, bid-ask spreads, and market liquidity for trading strategy and risk management insights.