Mohamed Nadeem

• Built real-time and batch data pipelines with AWS Serverless services such as Step Functions, Lambda, ActiveMQ, DynamoDB, S3, Kafka and SNS. • Streamlined processes and architected new data pipelines thereby reducing processing time upto 66%. • Analyzes and provides solutions for L3 support requests on data pipelines and microservices built on FastAPI.

• Developed reusable robust Python and PySpark programs with Airflow that processes 36TB+ of data for audience measurement. • Achieved a 20% reduction in processing time through PySpark optimization techniques such as proper partitioning, caching, broadcasting as well as rewriting SparkSQL scripts. • Collaborated with data scientists and business analysts to gather and analyze business requirements and deliver tailored solutions.

• Built and optimized SQL scripts for data cleansing and transformation tasks to enhance data quality, leading to improved accuracy in business reports. • Re-engineered data access solution to reduce the latency by 75%. This resulted in faster access for users to data in business reports. • Designed and developed data warehouses for reporting and analytics using multiple data modelling patterns and architecture and performed data quality assurance, transformations, standardization, reusability and access.

• Built and maintained real-time streaming data pipeline for network analysis and threat detection using Apache Spark, Kafka, HBase and PostgreSQL processing over 30 million events per day. • Built voice feedback analysis and recommendation system projects for eCommerce solution using machine learning and cloud tools such as Python Flask, Pandas, Tensorflow, Turicreate, IBM Watson. • Conducted code reviews and provided mentoring to junior team members, fostering continuous learning and improvement.

Government Engineering College, Thrissur