Danh is keen on applying Data Engineering and DataOps tools to solve real-world data-related challenges and help businesses build end-to-end data engineering solutions. He has experience collaborating with key stakeholders and acquiring domain knowledge to frame and solve data-related problems. Danh is passionate about designing, building, and deploying scalable and reliable data engineering systems.
He has sharpened his skills across the full stack of data engineering cycles, with strong capabilities and experience in designing, building, and deploying end-to-end data engineering solutions. These include data integration, data ingestion, data transformation, and data warehouse platforms on the cloud (AWS, Azure), using modern data stacks with infrastructure as code, with strong consideration for performance, scalability, and security. Danh was a software engineer, who has acquired strong programming skills in Python and SQL and has actively contributed to several open-source projects on GitHub. He is a team player with an open mind and is eager to collaborate.
Danh is currently building the data engineering platform for the Investment Data System team at HESTA, a super fund with billions under management and more than one million Australian members. Prior to this, Danh worked with the Pacific service team at Johnson Controls Australia, where his responsibilities included building data pipelines, developing and productionizing Dynamic Pricing models, modelling Customer churning risks on cloud, as well as performing data analysis and creating high-quality BI reports and KPI dashboards.
Danh has experience in programming and has served as a Data instructor at Monash Data Fluency, where he teaches hands-on data-related workshops in Python, Git, Bash, and High-Performance Computing to research students and staff at Monash University. He is passionate about data engineering and machine learning, and actively contributes to open-source projects like PyMC and Aesara on GitHub.
Danh was a Ph.D. researcher at Monash University, where he worked on Machine learning for intelligent transport systems. His research focused on understanding people’s choice behaviors when selecting their activity locations and travel modes. He applied various Machine learning methods (such as Bayesian methods, choice models, tree-based, and deep neural networks) with econometrics modeling approaches. Before that, he worked as a software developer and worked for several organizations in Vietnam.