Lucky Sharma
About Lucky Sharma
Lucky Sharma is a Data Engineer at Iris Software Inc. in Noida, Uttar Pradesh, India, with a background in Computer Science and Engineering from Chandigarh University and extensive experience in data engineering and software development.
Work at IRIS Software
Currently, Lucky Sharma holds the position of Data Engineer - Technology at Iris Software Inc. He has been with the company for six months, starting in 2024. The role is based in Noida, Uttar Pradesh, India, and follows a hybrid work model. In this position, he applies his expertise in data engineering to support various technology initiatives within the organization.
Education and Expertise
Lucky Sharma completed his Bachelor of Engineering in Computer Science and Engineering at Chandigarh University, achieving a score of 7.5 from 2015 to 2019. Prior to this, he studied at Sarvodaya Bal Vidyalaya, where he completed his Class XII CBSE with a score of 76.2% in PCM and Biology from 2012 to 2014. His educational background provides a strong foundation for his expertise in designing AWS Glue ETL pipelines and building ETL dashboards to analyze physical document status.
Professional Background
Before joining Iris Software, Lucky Sharma gained extensive experience in the tech industry. He worked at GlobalLogic as a Senior Software Engineer from 2022 to 2024 and as a Software Engineer from 2020 to 2022. He also served as an Associate Software Engineer at GlobalLogic for five months in 2019. His earlier experience includes an internship at Cognizant in 2019 and a project internship at Servosys Solutions. This diverse background has equipped him with a range of skills in data engineering and software development.
Achievements
Lucky Sharma has received the Spot Award of the Month three times for his outstanding work performance during his tenure at GlobalLogic. He developed a strategy that optimized job execution time, significantly reducing it from 12-14 hours to less than an hour for processing 600,000 images. Additionally, he implemented a job failure message user story that saved approximately 70% of debugging time, showcasing his ability to enhance operational efficiency.