Software Engineer with experience in Python, Flask, PySpark, Apache Spark, AWS, GCP, Kubernetes, and Snowflake. Has worked on full-stack risk management applications, real-time data pipelines, and machine learning models…
Software Engineer with experience in Python, Flask, PySpark, Apache Spark, AWS, GCP, Kubernetes, and Snowflake. Has worked on full-stack risk management applications, real-time data pipelines, and machine learning models, with experience across software engineering, cloud deployment, and data processing.
Designed and implemented a full-stack risk management application in Python and Flask, focusing on automating complex data-driven tasks such as hedge trade conversions and margin calculations.
Integrated advanced mathematical and statistical models, employing Generative AI techniques to simulate and predict financial scenarios, significantly enhancing predictive accuracy and risk assessment capabilities by 15%.
Devised advanced risk management strategies using straddle option techniques, applying complex mathematical concepts to real-world scenarios.
Led the migration of the application to Google Cloud, successfully deploying it on the App Engine.
Initiated the migration process to transition the application to a Java-based domain within the cloud, enhancing efficiency and reducing operational costs.
Achieved first place in a company-wide hackathon, securing a $1000 prize, developing a profitable trading algorithm that outperformed other submissions.
Designed and implemented a full-stack risk management application in Python and Flask, focusing on automating complex data-driven tasks such as hedge trade conversions and margin calculations
Integrated advanced mathematical and statistical models, employing Generative AI techniques to simulate and predict financial scenarios, significantly enhancing predictive accuracy and risk assessment capabilities by 15%.
Devised advanced risk management strategies using straddle option techniques, applying complex mathematical concepts to real-world scenarios.
Led the migration of the application to Google Cloud, successfully deploying it on the App Engine.
Initiated the migration process to transition the application to a Java-based domain within the cloud, enhancing efficiency and reducing operational costs.
Achieved first place in a company-wide hackathon, securing a $1000 prize, developing a profitable trading algorithm that outperformed other submissions.
Engineered a PySpark App, elevating efficiency by 60% through streamlined processes.
Utilized Python Apache Spark for queries, real-time streaming, Snowflake integration, and extensive data tasks.
Handled terabyte-scale data using Partitioning, Spark’s in-memory capabilities, and Broadcasts.
Led PySpark Jobs deployment on Kubernetes, notably accelerating data processing, enhancing efficiency, and boosting insights speed.
Designed real-time data pipelines with Apache Kafka and Spark, achieving a 75% performance boost in complex transformations.
Leveraged AWS services including Redshift, S3, Spectrum, and Athena to efficiently query large S3 data volumes, creating a virtual datalake.
Expertly utilized Kafka and Spark APIs for real-time data extraction, resulting in 80% reduced data loss.
Directly benefited business partners by contributing to quick market turnaround.
Automated data flow into Snowflake via Python scripts in AWS Lambda.
Devised a Linear Regression Model to extract insights on the correlation between patient reports and COVID-19.
Developed deep-learning models under the mentorship of Professor Hongye Liu to predict organ-specific cancer.
Employed Keras and TensorFlow to build a proactive model for effective disease indicator forecasting.
Engineered a PySpark App, elevating efficiency by 60% through streamlined processes.
Utilized Python Apache Spark for queries, real-time streaming, Snowflake integration, and extensive data tasks.
Handled terabyte-scale data using Partitioning, Spark’s in-memory capabilities, and Broadcasts.
Led PySpark Jobs deployment on Kubernetes, notably accelerating data processing, enhancing efficiency, and boosting insights speed.
Designed real-time data pipelines with Apache Kafka and Spark, achieving a 75% performance boost in complex transformations.
Leveraged AWS services including Redshift, S3, Spectrum, and Athena to efficiently query large S3 data volumes, creating a virtual datalake.
Expertly utilized Kafka and Spark APIs for real-time data extraction, resulting in 80% reduced data loss.
Directly benefited business partners by contributing to quick market turnaround.
Automated data flow into Snowflake via Python scripts in AWS Lambda.
Devised a Linear Regression Model to extract insights on the correlation between patient reports and COVID-19.
Developed deep-learning models under the mentorship of Professor Hongye Liu to predict organ-specific cancer.
Employed Keras and TensorFlow to build a proactive model for effective disease indicator forecasting.
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