Senthil Kumar S Top-Executive
• Cloud Certified Cloud Data Architect and Senior Data Engineer with 15 years strong background in designing and implementing scalable, secure, and efficient cloud-based data solutions. Proven expertise in cloud platforms such as AWS, Azure, GCP, Oracle Cloud Infrastructure, Nasuni and Snowflake.
Cloud Data Architecture & Engineering (AWS, Azure & GCP)
• Architected multi-cloud data platforms using Snowflake, Redshift, BigQuery, Apache Iceberg, and Azure Synapse Analytics (led initial pilot project and full-scale adoption).
• Built enterprise-grade real-time and batch data pipelines using Kafka, PySpark, AWS Glue, and Azure Data Factory for AI, reporting, and analytics workloads.
• 3+ years of experience designing and deploying cloud-native data solutions. Specialized in building scalable data pipelines, real-time data ingestion, and modern microservices-based applications on Google Kubernetes Engine (GKE). Proficient in leveraging Kafka connectors to bring streaming data from diverse systems into GCP. Deep expertise in BigQuery, Cloud Dataflow, and Airflow, with strong DevOps and infrastructure-as-code practices using Terraform.
• 2.5 years’ experience in Snowflake's architecture, data modelling, and performance tuning. Proven ability to lead data initiatives, ensuring high availability, scalability, and security of data systems. Expert in designing, implementing, and optimizing Snowflake-based data platforms, improving data performance and scalability.
• Designed Iceberg-based data lakes supporting time travel, schema evolution, and low-latency query performance.
• 6 years of experience with Amazon EC2, Amazon S3, Amazon RDS, VPC, IAM, Cloud Front, CloudWatch, SNS, SES, SQS, Glue, AWS Lambda, StateMachine, Bean stalk, elastic container service, Docker, CI/CD pipeline, API Gateway, Cloud Formation Template, SNS, S3, EMR and other services in Cloud platform (AWS).
AI/ML & Generative AI Architecture
• Designed and deployed production-grade AI/ML solutions using AWS SageMaker for model training, tuning, deployment (real-time/batch), and lifecycle management.
• Delivered Generative AI use cases by integrating Amazon Bedrock, OpenAI APIs, and Azure OpenAI with enterprise data using RAG pipelines, embeddings, vector databases, and prompt engineering.
• Integrated MLOps automation using SageMaker Pipelines, MLflow, and Azure ML.
Strategic Leadership & Cross-Functional Collaboration
• Served as Enterprise AI/ML & Data Architecture SME, influencing cloud modernization, AI, and GenAI strategy across business units.
