Binaya Khadka Email & Phone Number

Chicago, Illinois, United States

• Designed and implemented scalable data pipelines using Python and SQL, improving data processing efficiency by 25%.
• Developed and maintained ETL processes for data warehousing solutions using tools like Apache NiFi and Informatica.
• Implemented data validation and monitoring solutions, reducing data discrepancies and ensuring data integrity.
• Migrated on-premises databases to cloud platforms such as AWS and Azure, enhancing data accessibility and scalability while reducing operational costs by 15%.
• Extensively utilized AWS services including EC2 for virtual server management, S3 for scalable storage solutions, and Redshift for data warehousing and analytics, ensuring efficient and scalable data engineering.
• Collaborated with data scientists, analysts, and business stakeholders to understand data requirements and deliver actionable insights.

Charlotte, North Carolina, United States

• Designed and implemented robust data architecture solutions, enabling efficient data storage, retrieval, and analysis.
• Leveraged big data technologies such as Hadoop, Spark, and Kafka to process and analyze large datasets, enhancing data processing capabilities by 40%.
• Developed real-time data processing solutions using Apache Kafka, Spark Streaming, and PySpark, enabling instant data insights, resulting in improvement in customer experience through real-time analytics.
• Integrated diverse data sources, including relational databases, NoSQL databases, and third-party APIs, into a unified data platform.
• Automated data workflows and ETL processes using Apache Airflow, reducing manual intervention and improving process reliability.
• Ensured data security and compliance with industry standards and regulations, implementing encryption, access controls, and data masking techniques.

Richmond, Virginia, United States

• Conducted detailed data analysis using SQL, Excel, and Python, uncovering key insights and trends that informed business strategy and operations.
• Developed and maintained interactive dashboards and reports using tools like Tableau and Power BI, providing stakeholders with real-time access to critical data.
• Applied statistical techniques and machine learning models to analyze customer behavior, credit risk, and marketing effectiveness, contributing to a 20% increase in customer acquisition.
• Cleaned and transformed raw data into actionable datasets, ensuring data accuracy and consistency for reliable analysis and reporting.
• Worked closely with Data Engineers to enhance ETL processes, providing data quality feedback to refine pipelines for robust data handling.
• Supported machine learning initiatives by preparing and refining datasets for Data Scientists, aiding in model development and improvement.