• Built and deployed scalable data processing pipelines on Amazon Web Services (AWS) using AWS Glue and Athena for data transformation and analysis.• Designed S3-based data lakes to handle structured and unstructured data securely.• Created and optimized ETL workflows using AWS Lambda and Step Functions to process high volumes of financial transaction data.• Implemented data security and compliance measures in accordance with PCI DSS standards for banking.• Defined and managed data schemas for Redshift clusters, enabling efficient data warehousing and reporting.• Deployed Redshift Spectrum to query data directly from S3, reducing latency and improving performance.• Developed cost-monitoring dashboards using CloudWatch and integrated alerts for anomaly detection.• Worked with stakeholders to define data schemas and business requirements for reporting on banking KPIs.Led the migration of legacy ETL pipelines to modern, serverless workflows on AWS.• Conducted regular data audits to identify discrepancies and ensure pipeline efficiency and accuracy.• Designed scalable solutions for real-time data ingestion using Kinesis Streams and Firehose.

• Improved student understanding and performance by conducting Python and SQL code assignments and grading exams for a class of over 85 students, emphasizing data science and data visualization techniques.• Enhanced learning experience and decision-making skills by providing technical support and course content guidance during labs and discussions, collaborating with the instructor to ensure effective communication.• Maintained high standards of academic integrity and detail by ensuring accuracy in grading assignments and exams, applying attention to detail and problem-solving skills.• Fostered a collaborative and supportive learning environment by assisting students with lectures, labs, and assignments, holding office hours, and facilitating discussions to address student queries and promote effective communication.

• Collaborated with data analysts and developers to build scalable data pipelines on Google Cloud Platform (GCP).• Developed and maintained ETL processes using Google Dataflow and BigQuery to ingest and transform raw data.• Ensured data quality and reliability through the implementation of validation scripts and automated error-handling mechanisms.• Gained hands-on experience in Google Cloud Storage, Pub/Sub, and Cloud Composer (Apache Airflow) for orchestrating workflows.• Participated in team discussions for optimizing query performance in BigQuery.• Performed root cause analysis on production issues, ensuring minimal downtime and rapid resolution of pipeline failures.• Contributed to the design of a data warehouse schema to support analytical reporting needs.• Assisted in developing data ingestion frameworks for streaming and batch data using Apache Beam.• Supported documentation of workflows, ETL processes, and best practices to streamline onboarding for new team members.