Saibabu Karre
Data Engineer @S&P Global
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WORK HISTORY
Data Engineer @S&P Global
US
Design and maintain AWS-based financial data lake integrating diverse market sources via S3, Glue, and Snowflake, enabling real time analytics for over 500 enterprise stakeholders. • Build scalable PySpark data pipelines on AWS Glue and Spark SQL processing 8TB daily, improving job efficiency by 35% and reducing data latency through optimized partitioning. • Implement Kafka-based streaming ingestion frameworks supporting low-latency event-driven financial data flows, enhancing data freshness for global market intelligence and analytics teams. • Automate end-to-end ETL workflows using Apache Airflow, improving operational reliability, reducing manual interventions by 60%, and supporting daily data refresh schedules for business-critical dashboards. • Develop analytical data marts and transformation models in Snowflake leveraging dbt and SQL, increasing query performance by 40% while ensuring schema consistency across departments.
EDUCATION
Adelphi University
Master's degree
Mother Theresa College of Engineering & Technology
Bachelor of Technology
ABOUT SAIBABU KARRE
Data Engineer with 5+ years of experience building robust ETL pipelines, real-time streaming frameworks, and scalable cloud-based data solutions across finance, insurance, and automotive domains. Expertise in designing Big Data architectures with Apache Spark, Kafka, Hadoop, and Airflow to process large datasets and accelerate business insights. Proficient in AWS (Redshift, Glue, S3), GCP (BigQuery), and Azure (Synapse Analytics), with strong skills in Python, SQL, and PySpark for developing ETL jobs, APIs, and centralized data platforms. Experienced with Docker, Kubernetes, Tableau, and Power BI to ensure scalable, reliable pipelines and transform raw data into actionable intelligence for leadership and cross-functional teams.
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