Sai K.

About Sai K.

I build data platforms that take complex, multi-source data and turn it into reliable, analytics-ready systems used across the enterprise. With 8+ years in data engineering, I’ve worked across banking, healthcare, and retail designing scalable data pipelines on AWS, Azure, and GCP. My focus has been on building end-to-end data systems from ingestion and transformation to modeling and delivery ensuring data is accurate, consistent, and ready for business use. Most of my recent work has been around Snowflake and Spark-based platforms, where I’ve handled large-scale data processing, real-time ingestion, and enterprise data modeling. I’ve built systems that integrate APIs, RDBMS, and streaming data, and transformed them into structured, high-quality datasets for analytics and reporting. A few things I’ve delivered: • Built enterprise data lake and Snowflake ecosystem with 35+ fact and dimension tables • Designed real-time and batch pipelines using Kafka and Spark, enabling near real-time analytics • Migrated Hadoop-based systems to AWS lakehouse architecture, improving scalability and reducing cost • Built data validation and reconciliation frameworks improving data accuracy and trust • Reduced reporting turnaround time by 30%+ by delivering clean, analytics-ready datasets I focus on building systems that are scalable, maintainable, and easy to trust clean pipelines, strong data models, and reliable data quality frameworks. Data Engineering | Snowflake | PySpark | Apache Spark | AWS Glue | Lambda | Kafka | ETL | Data Pipelines | Lakehouse | Real-time Processing

Experience

1. Senior Data Engineer

   Truist

   Nov 2024 — Present · Charlotte, NC, US

   Building enterprise-scale data pipelines for risk and governance analytics by integrating multi-source data into a scalable AWS + Snowflake platform.Built ingestion pipelines using AWS Glue and Lambda for RDBMS, APIs, and cloud sourcesDesigned event-driven workflows using SNS, SQS, and Step Functions for reliable processingDeveloped PySpark pipelines on EMR for large-scale data transformation and enrichmentEstablished data lake on Amazon S3 with raw and staging layers for traceabilityBuilt and optimized data pipelines from S3 to Snowflake for analytics consumptionImplemented Medallion Architecture and designed fact/dimension models in SnowflakeDeveloped data validation, reconciliation, and standardization frameworksOptimized performance using clustering, partitioning, and query tuningAutomated CI/CD deployments and monitoring using CodePipeline and CloudWatchDelivered analytics-ready datasets, reducing reporting turnaround time by 30%

Education

• Eluru College of Engineering and Technology

  Bachelor of Engineering - BE

  2013 — 2017