May 2022 – Dec 2022 · 7 mos · Noida, Uttar Pradesh, India

Oct 2020 – Apr 2022 · 1 yr 6 mos · Bengaluru, Karnataka, India

• Worked on the main product, which involved development of robust scripts in python on AWS Glue Console and writing AWS Athena queries to generate unique serial numbers for parts of mobile devices in real-time.
• Responsible for development and management of Data Pipeline. ETL automation is done for files present in s3 locations using Python, PySpark as language and AWS glue as a platform. Data is then finally loaded to AWS Athena tables.
• Developed ETL scripts and automated bigdata workloads in Amazon EMR (HADOOP Based) environment.
• Responsible for post load processing of the data in final aggregate tables for further client usage.
• Optimized the PySpark jobs by fine tuning the spark and sql configurations.

Jan 2020 – Mar 2020 · 2 mos · Bangalore

• Did my training on AWS platform.
• Wrote a ETL script in python responsible for simultaneously updating MongoDB and SQL database from a single sql query.
• Worked in DEV and QA environment for development of ETL pipelines.

Jun 2019 – Jul 2019 · 1 mo · Mumbai Metropolitan Region

• Worked on Docsafe. A Blockchain based system which allows user to store their important documents on Auxledger which is Auxesis group’s developed blockchain network.
• Developed a prototype of Ride Sharing System on Hyperledger Fabric to make the whole taxi booking process tamper proof and this system will benefit the enterprise in keeping hackers and third parties away.
• Handled one of Auxesis's client and provided him with technical support.
• Learned basic development on Ethereum and Bitcoin platforms.

Jun 2018 – Jul 2018 · 1 mo · Jaipur Area, India

• Researched on numerous cyber security attacks possible on banks and suggesting ways to reducing malware attacks
• on banking apps and computers.
• Performed Malware analysis on the samples and drew conclusions concerning origin raison d'être, techniques used.
• Researched on Twitter Sentiment Analysis.
• Concluded that with the extensive amount of social media data available online in different forms such as videos and images, the conventional text-based sentiment analysis with ample research and analysis can be evolved into more complex models of multimodal sentiment analysis thus, improving the accuracy of traditional systems.