Maaz Shaikh
Lead Engineer @Samsung R&d Institute India - Bangalore
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WORK HISTORY
Lead Engineer @Samsung R&d Institute India - Bangalore
Bengaluru, IN
Led optimization of a 10PB Snowflake data platform supporting large-scale analytics workloads, reducing monthly compute/storage costs from $45K to $13K through architecture redesign and parallelized processing strategies.• Architected an intermediate columnar processing layer and event-driven Snowflake workloads orchestrated via Apache Airflow, improving query performance and reducing warehouse utilization by 60%+.• Designed an AI-driven Spark optimization platform leveraging LLM and RAG architectures to analyze historical job metadata and recommend cost-efficient configurations.• Built scalable data platform components focused on performance engineering, workload isolation, intelligent automation, and distributed Spark pipeline optimization.
EDUCATION
Don Bosco Institute of Technology (D.B.I.T)
Bachelor of Engineering - BE, Electrical, Electronics and Communications Engineering
ABOUT MAAZ SHAIKH
Lead Data Engineer with 6.5+ years of experience building large-scale data platforms, AI-driven pipelines, and high-performance lakehouse architectures across Samsung, Flipkart, and LTI.I specialize in designing scalable batch and real-time data systems using Apache Spark, Snowflake, and multi-cloud platforms (AWS, GCP, Azure). My work focuses on solving performance, scalability, and cost-efficiency challenges at petabyte scale — including optimizing a 10PB Snowflake ecosystem and building intelligent Spark optimization frameworks powered by LLM and RAG architectures.At Samsung R&D, I design production-grade data platforms supporting large-scale analytics workloads, implement event-driven parallel processing strategies, and build AI-assisted optimization layers to improve compute efficiency and developer productivity.Previously at Flipkart, I engineered high-volume data pipelines, modernized shipment processing systems, and developed near real-time streaming architectures delivering faster business insights while significantly reducing infrastructure costs.Core Areas:• AI-Driven Data Platforms & LLM/RAG Systems • Distributed Data Processing (Apache Spark, Flink, Kafka) • Snowflake & Modern Lakehouse Architectures • Data Platform Optimization & Performance Engineering • Multi-Cloud Data Engineering (AWS | GCP | Azure)Tech Stack: Spark | Snowflake | Python | Scala | PySpark | SQL | Airflow | Databricks | BigQuery | HadoopAlways interested in solving complex data engineering problems, building scalable AI-native
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