Deepanjit Singh Kohli
Sr. Business Intelligence Engineer at Amazon.com
I'm Deepan, currently working on Business Intelligence efforts at Amazon's Amazonian Experience and Technology (AET) division. With a Master’s in Management Sciences and Quantitative Methods, specializing in Data Science and Data Engineering, I’ve had the privilege of working on cutting-edge projects that integrate AI/ML, reporting, analytics, and data governance.
Throughout my career, I’ve had the opportunity to spearhead several impactful initiatives, such as enhancing product auto-classification efficiency, setting up comprehensive data infrastructures, and migrating critical payment systems to AWS. These projects have not only advanced operational efficiencies but also significantly contributed to Amazon's strategic goals. My work has been recognized with a top-tier rating at Amazon, and I’m currently pursuing a patent for a novel ETL infrastructure that leverages Generative AI to offer actionable insights.
I’m deeply committed to fostering growth and excellence within the data science and engineering community. My journey—from achieving a near-perfect GPA and earning Dean’s Excellence Scholarships to leading high-impact projects at Amazon—has been driven by a passion for innovation and a dedication to contributing to the broader field. I am excited to mentor and support others who are navigating their own paths in this dynamic and evolving landscape.
Whether you’re looking for guidance on technical skills, career advancement, or navigating complex projects, I’m here to offer insights and support to help you achieve your goals.
My Mentoring Topics
Data Science Fundamentals: Core concepts, statistical methods, and machine learning algorithms.
Data Engineering: ETL processes, data pipelines, data warehousing, and infrastructure design.
Business Intelligence Tools: Expertise in BI tools like Tableau, Power BI, and Looker.
Cloud Technologies: AWS services, cloud architecture, and migration strategies.
AI/ML Integration: Implementing AI/ML models, training techniques, and real-world applications.
Data Governance and Privacy: Best practices for data security, compliance, and privacy regulations.
Project Management and Leadership
Leading Data Projects: Managing large-scale projects, cross-functional teams, and project lifecycle.
Agile Methodologies: Implementing Agile practices, Scrum, and Kanban for effective project management.
CI/CD Pipelines: Best practices for continuous integration and deployment in data projects.