Tien Nguyen

As an Analytics Engineer/Data Engineer/Data Analytics at Brighthive.io, I played a key role in building and maintaining data infrastructure and analytics capabilities to support the organization's mission of using data to drive positive social impact. My responsibilities included:

• Collaborating with cross-functional teams to understand data needs and develop solutions for data collection, processing, storage, and analysis
• Building and maintaining data pipelines, ETL processes, and data models using technologies such as Python, SQL, DBT, Airbyte
• Designing and implementing analytics solutions using tools such as Apache Superset to support data-driven decision-making
• Developing and implementing quality control processes for data to ensure accuracy, completeness, and consistency
• Providing support and guidance to team members and stakeholders on data-related topics, including data modeling, database management, and analytics best practices

Through my work at Brighthive.io, I helped to enable data-driven decision-making across the organization, contributing to positive social impact in areas such as healthcare, education, and social services. By leveraging innovative tools like Airbyte, I was able to streamline data integration and ETL processes, enabling faster and more efficient data processing and analysis.

As a DevOps/Data Engineer at Parala Capital, I played a critical role in developing and maintaining the organization's data infrastructure and technology stack. My responsibilities included:

• Collaborating with cross-functional teams to understand data needs and develop solutions for data collection, processing, storage, and analysis
• Developing and maintaining ETL, ELT pipelines, data models, and data warehousing solutions using technologies such as Airbyte and DBT.
• Designing and implementing automated data quality control processes to ensure accuracy, completeness, and consistency of data
• Developing and maintaining infrastructure management processes using technologies such as Kubernetes and Terraform to support data engineering and application development workflows
• Working with AI models using Kubeflow to develop scalable and efficient ML pipelines
• Implemented and managed CI/CD pipelines for deploying applications, using GitLab CI/CD.
• Providing support and guidance to team members on data-related topics, including database management, data modeling, and analytics best practices
• Staying up-to-date with emerging trends and technologies in data and DevOps fields, and sharing knowledge with team to drive continuous improvement

Through my work at Parala Capital, I helped to drive investment decision-making processes through data insights and analytics. My ability to develop efficient and reliable data pipelines, coupled with my experience in infrastructure management and AI model development, allowed me to create a scalable infrastructure that was integral to the success of the organization. My expertise in technologies such as Kubernetes, Terraform, Python, SQL, and automated data quality control processes, as well as working with AI models using Kubeflow, made me an invaluable member of the team and allowed us to stay at the forefront of innovation in the data engineering, DevOps, and AI space.