Naim Panjwani is a Lead Data Engineer with over a decade of experience building scalable, parallel data-intensive applications and ETL pipelines across finance and genomics. He combines deep statistical and bioinformatics expertise—demonstrated by publications in top journals—with hands-on engineering managing 700+ production pipelines on Azure, OpenShift and Airflow. His background spans end-to-end analytics: GWAS annotation web apps, custom reference panels for imputation, large-scale HPC workflows, and ML-enabled data quality monitoring. A proven mentor and instructor, he has taught practical data analytics and visualization while guiding cross-functional teams to deliver robust data platforms. Notably, he bridges wet-lab science and cloud engineering, translating complex biological problems into reproducible, production-grade pipelines. Insatiably curious, he continuously adopts new tools to solve interdisciplinary physiological, financial and computational challenges.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Science, Biochemistry; Biotechnology Specialization, Bachelor of Science, Biochemistry; Biotechnology Specialization at University of Waterloo
Master of Science (M.Sc.), Biostatistics, Master of Science (M.Sc.), Biostatistics at Dalla Lana School of Public Health, University of Toronto
MSc, Institute of Medical Science, A+, MSc, Institute of Medical Science, A+ at Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto
Certification in Data Analytics and Visualization, Computer Science, A+, Certification in Data Analytics and Visualization, Computer Science, A+ at University of Toronto School of Continuing Studies
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