Sai Kiran Karnati
Data Engineer, Security(Ai), Cloud Data Platform @Lumen Technologies
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WORK HISTORY
Data Engineer, Security(Ai), Cloud Data Platform @Lumen Technologies
Designed, developed, and maintained ETL/ELT data pipelines using Python and SQL, ingesting and transforming data from multiple sources into analytics-ready datasets for reporting and data science workloads.• Deployed and supported cloud-based data pipelines primarily on Azure, with exposure to AWS and GCP environments, ensuring reliability, scalability, and alignment with enterprise data standards.• Built and optimized complex SQL queries using joins, CTEs, and aggregations to support analytics, reconciliation, investigations, and BI reporting. • Implemented data quality and validation frameworks, improving dataset accuracy by 25%, completeness by 30%, and consistency for downstream consumers• Developed and optimized data models and schemas to support analytics, reporting, and machine learning workflows.• Applied performance optimization techniques including query tuning, schema refactoring, and partitioning strategies to improve processing efficiency by 35% and query performance by 40%.• Collaborated closely with data scientists and analysts to design data solutions that integrate with BI tools and ML models.
EDUCATION
Vel Tech Technical University
Bachelor of Science, Computer Science
University of North Texas
Master of Science
ABOUT SAI KIRAN KARNATI
Data Engineer & AI Security Specialist | Building Secure, Scalable Data Pipelines & Threat Intelligence Systems | Azure | Python | SQL | RAG | Databricks.I design, develop, and maintain robust ETL/ELT pipelines that turn raw, multi-source data into clean, analytics-ready datasets powering reporting, data science, and machine learning initiatives. With hands-on experience across Azure (primary), AWS, and GCP, I ensure pipelines are reliable, scalable, and fully aligned with enterprise governance and security standards.My work spans performance optimization—query tuning, schema refactoring, partitioning, and indexing—to deliver 35–40% gains in processing efficiency and query speed. I’ve implemented data quality frameworks that boosted accuracy by 25%, completeness by 30%, and consistency for downstream teams. I collaborate closely with data scientists, analysts, and cybersecurity stakeholders to translate business needs into production-grade solutions.In the cybersecurity domain, I’ve built secure, high-throughput environments using Azure Confidential VMs and shielded containers to safely analyze malware samples weekly. I architected an end-to-end Retrieval-Augmented Generation (RAG) pipeline (Azure OpenAI + AI Search + LangChain/Semantic Kernel) that enables natural-language querying across petabytes of threat intelligence, slashing analyst investigation time by 68%.I’ve delivered fault-tolerant.NET 8 microservices for malware sandboxing and behavioral extraction, Python-based agentic AI assistants (Semantic Kernel) adopted by 200+ global SOC analysts for alert triage, IOC enrichment, and incident reporting, and KQL-based behavioral detections in Azure Sentinel that improved insider-threat mean-time-to-detect (MTTD) by 55%.Security is core to everything I build: zero-trust service mesh with auto credential rotation, exactly-once processing patterns, automated SAST/DAST + dependency scanning in CI/CD (reducing vulnerability escape rate from 12% to <2%), and migration of legacy GCP pipelines to a unified Azure + Databricks architecture with strict schema enforcement.I’m passionate about secure data innovation at the intersection of engineering, AI, and cybersecurity. Always open to connecting on challenging data/AI/security projects, cross-team collaborations, or new opportunities.Let’s talk pipelines, threat intel, or Gen AIfeel free to reach out!
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