Driven Computer science graduate with expertise in data engineering, machine learning ops and cloud computing. Passionate about leveraging analytical horsepower and automation to enable data-driven decision making. Proven ability to unlock business value through well-architected analytics solutions. Currently working on building this page

• Programming & Scripting: Python, R, SQL, Scala, Bash
• ML Frameworks: Scikit-Learn, PyTorch, TensorFlow
• Data & Tools: • ETL, Databricks, Streamsets, Airflow
• Databases: MySQL, Postgres, SQL Server, MongoDB, DynamoDB, ArangoDB, Teradata
• Data warehouse: Snowflake, AWS Redshift
• Bigdata tools: Airflow, Apache Spark, Nifi, Confluent Kafka, Glue, EMR, Hadoop
• Cloud Platforms: AWS, Azure
• Tools: Docker, Spark, Kubernetes

• Streamsets White belt (Certified)
• AWS Solutions Architect - Associate (Certified)
• AWS Cloud Practitioner - Foundational (Certified)
• Data science Lab - World Quant university (Certified)
• Nvidia - Deep learning foundations (Certified)
• Snowflake - Data Warehouse (Certified)
• Databricks spark associate developer (Trained)
• SnowPro Data engineer (Trained)

• Evaluated deep learning models like MLP and Boosting algorithms to detect anomalies
• Achieved 98% precision score, outperforming baseline by 5%

• Created a GARCH time series model for predicting asset volatility, acquired stock data through an API,
• Cleaned and stored it in an SQLite database, and built my own API to serve model predictions.

• Architected cloud ETL process loading 300+ GB/day into DynamoDB
• Enhanced reliability through validation rules in Python

• Created customer 360 model improving PEP identification accuracy by 15%
• Operationalized training through MLOps monitoring

• 📫 Reach me at [email protected]
• LinkedIn