Developed tumbling window support for event data models in both Python and TypeScript, focusing on AWS Lambda streaming workloads. Work in the aws-powertools/powertools-lambda-python and aws-powertools/powertools-lambda-typescript repositories enabled time-based aggregation for Kinesis and DynamoDB streams by extending event data classes and implementing robust parsing logic. Enhanced schema validation and added comprehensive tests to ensure correctness of windowed analytics within Lambda functions. The approach strengthened consistency between event models and parsing, laying a foundation for reliable time-based data processing. Leveraged skills in event processing, schema validation, and data modeling to address the needs of scalable, windowed stream analytics.