Tùng Phạm

With hands-on experience in Backend Development and Data Engineering, I focus on building scalable, high-performance systems for large-scale data processing and automation. My goal is to continuously improve my expertise in distributed systems, data pipelines, and modern data infrastructure technologies such as Kafka, Spark, and Airflow. I am passionate about developing reliable backend architectures and efficient data workflows that can handle large volumes of data with stability and maintainability.

Beyond technical implementation, I aim to contribute as a strong technical contributor who helps optimize system performance, improve operational efficiency, and support data-driven decision making. I continuously strive to learn modern technologies, strengthen problem-solving skills, and deliver sustainable, high-quality engineering solutions.

Data Engineer at Ciaolink Studio (2026-03 – 2026-05)

Teamsize: 3 members

• Built a Spark Cluster (Master-Worker) on Linux. Used Tailscale and NFS to ensure the system can easily add more nodes in the future.
• Extracted ~250k objects from MongoDB and used PySpark to convert them into Parquet
• Applied Medallion Architecture (Bronze, Silver, Gold) to ensure data quality and easy tracking as data grows.
• Used Apache Airflow and MySQL to manage ETL tasks, making it easy to monitor and scale the number of jobs.
• Tuned Spark memory and used Data Partitioning to keep the system fast even when data volume increases.

Python Developer at Ciaolink Studio (2024-09 – 2026-05)

Teamsize: 6 members

• Pioneered the use of the Parquet format for historical data storage, achieving significant reductions in storage footprint and drastically improving I/O performance (read speeds).
• Leveraged Polars (for parallel processing) and Pandas to build a high-performance engine for analyzing massive time-series datasets.
• Conducted rigorous Backtesting of various trading strategies against historical data, utilizing the custom data processing engine.
• Researched and prototyped a real-time data streaming pipeline incorporating Kafka within a Docker container environment.

Data Engineer + Python Developer at Ciaolink Studio (2019-09 – 2024-08)

Teamsize: 4 members

• Designed and developed a multi-source data ingestion pipeline using Python, Selenium, and BeautifulSoup for large-scale web data extraction.
• Implemented dynamic proxy rotation and automated scraping workflows to improve system stability and reduce anti-bot detection issues.
• Processed approximately 30 million records daily from search engines and collected over 300K JSON profile datasets from social media and online platforms.
• Managed large-scale datasets including approximately 600 million records on MySQL and 100 million records on MongoDB systems.
• Designed a staging data architecture using daily schemas to support high-volume batch processing workflows efficiently.
• Implemented MySQL horizontal scaling and partitioning strategies to reduce table locking and improve query performance on massive datasets.
• Optimized high-volume insert and update operations using batch processing techniques and MySQL performance tuning.
• Built internal API services using Node.js, FastAPI to coordinate distributed worker processing tasks.
• Developed automated backup and basic failover workflows to improve system reliability and operational stability.
• Built core job change detection logic by comparing daily incoming profile data against historical datasets to identify career movement and profile updates.
• Automated reporting workflows for scrape volume, data quality validation, and keyword trend analysis.
• Standardized processed data exports into CSV, JSONL, and Parquet formats with automated uploads to Amazon S3 for downstream systems.

Python Developer at NCompany (2019-06 – 2019-09)

Trainer at BCompany (2019-03 – 2019-05)

• Build rasa chatbot

– Ha Noi University of Science and Technology (2012-09 – 2017-10)