Sai Kumar

About Sai Kumar

I’m a cloud-focused Data Engineer who enjoys turning complex, large-scale data into…

Experience

1. Senior Data Engineer

   Enbridge

   Nov 2025 — Present · US

   Migrated on-premise and legacy ETL workloads to AWS using Glue, S3, and PySpark, modernizing data pipelines and improving scalability, fault tolerance, and processing efficiency. • Designed and implemented end-to-end ELT pipelines using AWS Glue, Snowflake, and dbt, enabling efficient ingestion and transformation of structured and semi-structured data. • Built and optimized data lake architecture using Amazon S3 and Apache Iceberg, supporting ACID transactions, schema evolution, and time-travel capabilities for large-scale datasets. • Developed reusable and modular dbt models to transform raw data into curated data marts, improving data accessibility and reducing reporting latency for business users. • Implemented real-time and near real-time ingestion pipelines using AWS Lambda, Kinesis, and EventBridge to support streaming analytics and operational use cases. • Designed and developed LLM-powered data solutions, including automated SQL generation and data discovery tools, improving analyst productivity and reducing manual query effort. • Built Retrieval-Augmented Generation (RAG) pipelines by integrating structured data from Snowflake with vector-based search, enabling intelligent search and question-answering capabilities. • Developed pipelines for embedding generation and vector storage using Python-based frameworks and vector databases (FAISS/Pinecone), enabling semantic search across enterprise datasets. • Integrated AI-driven data quality and validation frameworks, leveraging anomaly detection and rule-based checks to improve data reliability and reduce pipeline failures. • Optimized Snowflake performance using clustering, query tuning, and warehouse right-sizing, reducing compute costs while improving query performance and concurrency.